A method and device for matching hotel room types

Abstract

The application discloses a hotel room type matching method and device, comprising: obtaining the original information of a newly added hotel and room type, preprocessing the original information, and generating a standardized hotel and room type feature data set; constructing a multi-level feature vector based on the standardized feature data set; screening and generating a candidate matching set by combining an inverted index and a local sensitive hashing algorithm; calculating a matching score matrix by fusing a rule matching model, a machine learning model and a deep learning model; generating a final matching conclusion by Bayesian uncertainty estimation and threshold dynamic adjustment; and optimizing the matching model based on the matching conclusion and business feedback data. The application solves the accuracy problem of hotel room type cross-supplier matching by multi-dimensional feature extraction, multi-model fusion matching and confidence evaluation, and improves the matching efficiency and robustness.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, in particular to a hotel room type matching method and device, which is suitable for cross-supplier hotel and room type resource integration and matching in the tourism accommodation industry. BACKGROUND

[0002] As an intermediary connecting users and hotel resources, hotel booking platforms usually need to integrate resources from multiple hotel suppliers to provide rich choices. In the tourism accommodation industry, hotels and room types are typical non-standardized resources, unlike standardized goods that have unified product codes or identification marks.

[0003] The hotel matching technology commonly used in the industry at present mainly relies on simple name matching and geographic location matching. For example, some systems use a string similarity algorithm based on hotel names for matching, or use latitude and longitude information to find possible same hotels within a certain radius. Some other platforms solve the cross-supplier hotel identification problem by manually maintaining a hotel mapping table.

[0004] More advanced hotel matching technologies usually use multi-feature fusion, taking hotel names, addresses, star ratings, facilities, etc. as feature vectors, and calculating the distance between feature vectors to determine whether they are the same hotel. Such technologies perform well when dealing with structured data, but when faced with heterogeneous data formats and different description methods provided by different suppliers, the matching accuracy is often not ideal, especially when dealing with hotels in the same city with the same brand chain or similar names but actually different hotels.

[0005] The main problems existing in the prior art include: first, there is a lack of multi-dimensional feature extraction and weight dynamic adjustment mechanism for hotels and room types, resulting in insufficient matching accuracy in complex scenarios; second, there is no unified confidence evaluation system to quantify the reliability of the matching results; in addition, most systems fail to effectively use historical matching data for model optimization, lack self-learning and evolution ability, and require frequent manual intervention to correct incorrect matching. SUMMARY

[0006] The purpose of the present application is to provide a hotel room type matching method and device, which aims to solve the technical problems of insufficient cross-supplier hotel room type matching accuracy, lack of confidence evaluation and self-optimization ability in the prior art.

[0007] To achieve the above purpose, the present application provides a hotel room type matching method, comprising:

[0008] Obtaining the original information of the newly added hotel and room type, preprocessing the original information, and generating a standardized hotel and room type feature data set;

[0009] Based on the standardized hotel and room type feature data set, a basic feature vector, a semantic feature vector and a geographic space feature vector are constructed through feature engineering to obtain a multi-level feature vector;

[0010] Based on the multi-level feature vector, an inverted index and a local sensitive hashing algorithm are combined to filter from the platform full hotel and room type data to generate a candidate matching set;

[0011] Based on the candidate matching set, a matching model is constructed through a rule matching model, a machine learning model and a deep learning model, and then a matching score matrix is fused to generate a preliminary matching result;

[0012] Based on the preliminary matching result and the matching score matrix, a Bayesian uncertainty estimation and a threshold dynamic adjustment are used to generate a final matching conclusion including a matching state, a matching object ID and a matching confidence;

[0013] Based on the final matching conclusion and the collected business feedback data, the parameters and thresholds of the matching model are optimized through online learning and regular batch training to obtain a parameter-optimized matching model.

[0014] Preferably, the preprocessing of the original information to generate a standardized hotel and room type feature data set comprises:

[0015] Based on the original information, the parsed original information is subjected to special character removal, unified case and missing value processing to obtain a data cleaning result;

[0016] The data cleaning result is subjected to field mapping to a unified standard field set to generate a field standardization result;

[0017] The hotel name, hotel address and room type name in the field standardization result are subjected to word segmentation, stop word removal and entity recognition processing to obtain a text structured result;

[0018] Based on the text structured result, a standardized geographic coordinate and administrative division code are obtained through a geographic coding service to generate the standardized hotel and room type feature data set.

[0019] Preferably, the construction of a basic feature vector, a semantic feature vector and a geographic space feature vector through feature engineering to obtain a multi-level feature vector comprises:

[0020] Based on the standardized hotel and room type feature data set, a hotel name, a hotel star rating, a hotel address and a room type name, a room type area and a bed type feature are extracted to construct a basic feature vector;

[0021] The text information in the standardized hotel and room type feature dataset is converted using a pre-trained language model to generate a semantic feature vector;

[0022] Based on the geographic information in the standardized hotel and room type feature dataset, a geographic spatial feature vector including latitude and longitude coordinates and administrative division codes is constructed;

[0023] The base feature vector, the semantic feature vector, and the geographic spatial feature vector are subjected to weight fusion and dimensionality reduction processing to generate the multi-level feature vector.

[0024] Preferably, the combination of inverted index and locality-sensitive hashing algorithm filters from the platform full hotel and room type data to generate a candidate matching set, including:

[0025] Based on the multi-level feature vector, an inverted index based on hotel name and hotel address is constructed, and the candidate hotel subset is obtained through fast filtering of the city, administrative division, and star rating attributes where the hotel is located;

[0026] The multi-level feature vector is applied to the locality-sensitive hashing algorithm to map to a low-dimensional hash code, and a set of potential similar objects is found;

[0027] The name similarity, geographic location similarity, and facility similarity of the set of potential similar objects are calculated to obtain a multi-dimensional similarity index;

[0028] Based on the weighted score of the multi-dimensional similarity index, sorting and truncation are performed to generate the candidate matching set.

[0029] Preferably, the matching model is constructed by a rule matching model, a machine learning model, and a deep learning model, and then a matching score matrix is fused and calculated to generate a preliminary matching result, including:

[0030] Based on the candidate matching set, a rule matching score is obtained by scoring based on hard matching rules and soft matching rules;

[0031] The candidate matching set is scored using a random forest, gradient boosting tree, and support vector machine model to obtain a machine learning matching score;

[0032] The candidate matching set is scored by a twin network and an attention mechanism network to obtain a deep learning matching score;

[0033] The rule matching score, the machine learning matching score, and the deep learning matching score are weighted and averaged to generate the matching score matrix and the preliminary matching result.

[0034] Preferably, the generating the final matching conclusion including matching state, matching object ID and matching confidence by Bayesian uncertainty estimation and threshold dynamic adjustment comprises:

[0035] Based on the preliminary matching result, analyzing the distribution characteristics of the matching score, calculating the gap between the highest score and the second highest score to obtain the score distribution characteristics;

[0036] Applying Bayesian method to estimate the uncertainty of the matching result in combination with historical matching data based on the score distribution characteristics to generate a confidence index;

[0037] Based on the confidence index, combining business requirements, dynamically adjusting the matching decision threshold according to the accuracy and recall rate indexes to obtain the adjusted decision threshold;

[0038] Based on the matching score matrix and the confidence index, constructing a decision matrix to divide the preliminary matching result into four categories: certain matching, possible matching, certain non-matching and manual review;

[0039] Based on the decision matrix and the adjusted decision threshold, determining the matching state and the matching object ID, calculating the matching confidence, and generating the final matching conclusion.

[0040] Preferably, the generating the multi-level feature vector further comprises feature representation learning based on frequency domain transformation, comprising:

[0041] Performing discrete Fourier transform on the standardized hotel and room type feature dataset to convert multi-dimensional data from time domain to frequency domain to obtain frequency domain representation data;

[0042] Based on the frequency domain representation data, performing feature compression and reconstruction through a frequency domain autoencoder network to obtain frequency domain feature representation;

[0043] Performing spectral energy distribution analysis on the frequency domain feature representation to determine the key frequency interval to obtain the optimized frequency domain feature;

[0044] Fusing the optimized frequency domain feature with the basic feature vector, the semantic feature vector and the geospatial feature vector to generate an enhanced multi-level feature vector.

[0045] Preferably, the generating the preliminary matching result further comprises topology optimization of the sparse evolutionary neural network, comprising:

[0046] Based on the candidate matching set, constructing a sparse MLP network structure, defining a topology search space including the number of layers, the number of neurons and the connection mode to obtain an initial network topology;

[0047] For the initial network topology, the optimal topology structure is searched in the search space by a genetic algorithm, and an adaptive function combining matching accuracy and model complexity is used for evaluation to obtain an optimized network topology;

[0048] Based on the optimized network topology, the interdependence between features is analyzed, and highly correlated features are grouped into the same module to form a modularization grouping structure;

[0049] For the modularization grouping structure, a local dense and global sparse connection mode is used for network training to obtain a sparse evolutionary matching model;

[0050] The sparse evolutionary matching model is used to score the candidate matching set to generate an optimized matching score matrix and the preliminary matching result.

[0051] Preferably, generating the final matching conclusion also includes resource adaptive allocation based on the contrast ranking network, including:

[0052] Based on the candidate matching set, a contrast ranking network is constructed to input the basic feature vector pair of the hotel matching candidate pair for contrast to obtain a foreground score;

[0053] The foreground score is processed in layers, and the hotel matching candidate pair is divided into high, medium and low three levels according to the foreground score to obtain a layered candidate set;

[0054] Based on the layered candidate set, the hotel matching candidate pair with high foreground score is allocated deep feature extraction and multi-model fusion scoring resources, the hotel matching candidate pair with medium foreground score is allocated lightweight model evaluation resources, and the hotel matching candidate pair with low foreground score is allocated basic rule judgment resources to obtain a layered evaluation result;

[0055] The layered evaluation result is matched for quality monitoring, and when the accuracy decreases by more than a preset threshold, the weight and sampling ratio of the contrast ranking network are automatically adjusted to obtain an adaptive adjustment result;

[0056] Based on the adaptive adjustment result and the layered evaluation result, a resource-optimized matching score matrix and the final matching conclusion are generated.

[0057] Preferably, the parameters and thresholds of the matching model are optimized by online learning and periodic batch training, including:

[0058] Based on the final matching conclusion, the key data and intermediate results in the matching process are persistently stored to establish a matching history database;

[0059] For the matching history database, the confirmation, correction and negative feedback information of the matching result of the business system are collected to obtain business feedback data;

[0060] Based on the service feedback data, the accuracy, recall rate and F1 score indicators are calculated, time series analysis is performed, and matching quality monitoring results are obtained;

[0061] For the matching quality monitoring results, the online learning method is used to fine-tune the parameters and decision thresholds of the matching model in real time, and real-time optimization results are obtained;

[0062] Based on the real-time optimization results and service feedback data, the matching model is periodically retrained in batches, the parameters of the matching model are updated, and the parameter-optimized matching model is generated.

[0063] The application also provides a hotel room type matching device, comprising:

[0064] A data preprocessing module is configured to acquire original information of new hotels and room types, preprocess the original information, and generate standardized hotel and room type feature data sets;

[0065] A feature construction module is configured to construct basic feature vectors, semantic feature vectors and geographic spatial feature vectors based on the standardized hotel and room type feature data sets through feature engineering, and obtain multi-level feature vectors;

[0066] A candidate generation module is configured to filter platform full hotel and room type data based on the multi-level feature vectors, combine inverted indexes and local sensitive hashing algorithms, and generate a candidate matching set;

[0067] A matching score module is configured to construct a matching model based on the candidate matching set through a rule matching model, a machine learning model and a deep learning model, and then fuse and calculate a matching score matrix to generate a preliminary matching result;

[0068] A decision module is configured to generate a final matching conclusion including a matching state, a matching object ID and a matching confidence by Bayesian uncertainty estimation and threshold dynamic adjustment based on the preliminary matching result and the matching score matrix;

[0069] An optimization module is configured to optimize the parameters and thresholds of the matching model through online learning and periodic batch training based on the final matching conclusion and collected service feedback data, and obtain a parameter-optimized matching model.

[0070] The application has the following advantages:

[0071] 1. Through multi-dimensional feature data acquisition and preprocessing, the standardization processing of different supplier heterogeneous data is realized, and the data quality is improved;

[0072] 2. Through multi-level feature vector construction, the feature information of hotels and room types is comprehensively captured, and the accuracy of matching is enhanced;

[0073] 3. Through the candidate matching set generation, the search space is effectively reduced, and the matching efficiency is improved;

[0074] 4. Through multi-model fusion matching and scoring, the advantages of different models are integrated, and the robustness of the matching is improved;

[0075] 5. Through confidence evaluation and decision-making, the reliability of the matching result is quantified, and business decisions are supported;

[0076] 6. Through the feedback of the matching result and the optimization of the model, the self-learning and evolution of the system are realized, and the matching quality is continuously improved. BRIEF DESCRIPTION OF DRAWINGS

[0077] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0078] Figure 1 The flowchart of the hotel room type matching method provided by the embodiment of the present application;

[0079] Figure 2 The flowchart of the multi-level feature vector construction provided by the embodiment of the present application;

[0080] Figure 3 The structural block diagram of the hotel room type matching device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0081] In order to make the person skilled in the art better understand the present application, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application.

[0082] In some of the descriptions in the specification and claims of the present application and the above-mentioned drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed or in parallel without the order appearing in the text. The serial numbers of the operations, such as S1, S2, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or less operations, and these operations can be executed in sequence or in parallel. It should be noted that the "first", "second", etc. in this paper are used to distinguish different messages, devices, modules, etc., and do not represent the sequence, nor do "first" and "second" represent different types.

[0083] It is to be understood that the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It is further understood that the terms "comprise" (or comprise), "comprises" (or comprises) and "comprising" (or comprising), when used in this specification, specify the presence of stated features, integers, steps, operations, elements, or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or groups thereof. It is further understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements can be present. In addition, the word "connected" or "coupled" as used herein can include wirelessly connected or wirelessly coupled. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0084] It is to be understood that the terms so used are intended to encompass the general meaning of such terms as well as the specific meaning that can be defined herein, unless otherwise defined in the specification. It is further to be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the specification and relevant art and should not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

[0085] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application, in which the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0086] Please refer to Figure 1 , Figure 1 The flowchart of the hotel room type matching method provided by the embodiments of the present application is shown in FIG. 1. As shown in FIG. 1, the hotel room type matching method provided by the embodiments of the present application includes the following steps: Figure 1

[0087] Step S1: Obtain the original information of the newly added hotel and room type, pre-process the original information, and generate a standardized hotel and room type feature data set.

[0088] ​In this step, the newly added hotel and room type raw data from different hotel suppliers are first received. These raw data usually contain a large amount of unstructured or semi-structured information, such as hotel name, address, contact information, facility list, room type description, etc. Due to the different data formats and standards of different suppliers, the heterogeneous data needs to be uniformly processed. The preprocessing process first parses the raw data in different formats and extracts key fields; then performs data cleaning, including removing special characters, unifying case, handling missing values and correcting obvious errors; then structures the text information through natural language processing techniques such as word segmentation, stop word removal and entity recognition to extract key information; at the same time, it also obtains standardized geographic coordinates and administrative division codes based on address information, and calculates the relative position relationship with surrounding landmarks. In addition, it will also complete the key missing fields through rule inference or external API calls, such as inferring the administrative division from the address, inferring the brand from the hotel name, etc. Finally, the cleaned and standardized data is quality evaluated, and the completeness, consistency and reliability scores of each dimension data are calculated to provide the basis for subsequent feature weight allocation. Through this series of processing, the original heterogeneous data is converted into a standardized hotel and room type feature dataset, laying the foundation for the construction of feature vectors in the subsequent steps.

[0089] Step S2: Based on the standardized hotel and room type feature dataset, construct the basic feature vector, semantic feature vector and geographic spatial feature vector through feature engineering to obtain a multi-level feature vector.

[0090] In this step, based on the standardized feature dataset generated in step S1, a multi-level feature vector is constructed to fully capture the feature information of hotels and room types. The feature vector construction is divided into multiple levels: first, the basic feature vector, including hotel name, star rating, address, phone number, facility list, and room type name, area, bed type, capacity, etc. structured information; second, the semantic feature vector, which uses pre-trained language models (such as BERT, Word2Vec, etc.) to convert hotel name, room type name, hotel description, etc. text information into high-dimensional semantic vectors to capture the deep semantic information of the text; then the geographic spatial feature vector, based on standardized geographic information, constructs spatial features including latitude and longitude coordinates, administrative division codes, distances to major landmarks, etc.; in addition, business features such as hotel price range, rating, room type price range, promotion strategy, etc. are extracted from a business perspective to construct a business feature vector. Then, based on historical matching data and domain knowledge, the importance of each feature dimension is evaluated and a reasonable weight coefficient is assigned. Finally, the multi-dimensional feature vectors are fused according to the weight, and dimensionality reduction is performed through principal component analysis (PCA) or t-SNE, etc. to generate the final comprehensive feature vector. This multi-level feature vector construction method can fully capture the various features of hotels and room types, providing a rich information foundation for subsequent matching.

[0091] In the specific implementation of the present step, the feature representation learning based on frequency domain transformation is a key innovation. The frequency domain autoencoder network adopts a 6-layer structure: the input layer receives standardized feature data with a dimension of the original feature dimension; the encoder includes two hidden layers with a number of neurons of 80% and 50% of the original dimension, respectively, using a ReLU activation function; the decoder also includes two hidden layers with a number of neurons symmetric to the encoder, also using a ReLU activation function; and the output layer corresponds to the frequency domain representation. The training data is derived from 500,000 pairs of confirmed matching hotel information accumulated by the platform, and the training set and the validation set are divided in a ratio of 7:3. The training adopts the Adam optimizer, the learning rate is set to 0.001, the batch size is 64, and the training rounds are 100. In the design of the frequency domain reconstruction loss function, the weighted mean square error (MSE) is adopted, in which the weight of the low-frequency component (the first 30% of the frequency spectrum) is 0.7, the weight of the medium-frequency component (30%-70% of the frequency spectrum) is 0.2, and the weight of the high-frequency component (more than 70% of the frequency spectrum) is 0.1. In the experimental verification, the model using only 30% of the low-frequency component reached a matching accuracy of 95.3% on the validation set, with a reduction of 63.5% in computational complexity, and an increase of 16.8 percentage points in accuracy compared with the benchmark model when processing supplier data with low standardization.

[0092] Step S3: Based on the multi-level feature vector, the inverted index and the local sensitive hashing algorithm are combined to filter from the platform full hotel and room type data to generate a candidate matching set.

[0093] In this step, it is necessary to quickly filter out the candidate set that may match from the platform full hotel and room type data (which may contain hundreds of thousands or even millions of records) to reduce the computational complexity of subsequent fine matching. To achieve efficient screening, first, based on the platform full data, construct a multi-level inverted index and a geographic spatial index to support efficient similarity retrieval. The screening process adopts a multi-level strategy: first, perform coarse-grained quick filtering based on key attributes such as the city, administrative division, and star rating of the hotel to quickly narrow down the search space; then apply the Locality-Sensitive Hashing (LSH) algorithm to the multi-level feature vector constructed in step S2 to map high-dimensional vectors to low-dimensional hash codes and quickly find potentially similar hotels and room types; next, calculate multi-dimensional similarity indicators such as name similarity (using edit distance, Jaccard coefficient, etc.), geographic location similarity (based on geographic distance), and facility similarity for the filtered candidate set; finally, based on the weighted scores of the multi-dimensional similarity indicators, sort the candidate matching objects and select the top N as the candidate matching set. At the same time, the system will also analyze the feature differences between each hotel / room type in the candidate matching set and the target object to provide a basis for subsequent fine matching. This multi-level screening strategy can significantly reduce the computational load of subsequent fine matching while ensuring recall rate, improving overall matching efficiency.

[0094] Step S4: Based on the candidate matching set, construct a matching model through a rule matching model, a machine learning model, and a deep learning model, and then fuse the matching score matrix to generate a preliminary matching result.

[0095] In this step, the candidate matching set generated in step S3 is fine-matched and scored, and a multi-model fusion method is used to improve the matching accuracy. The matching score includes three types of models: first, a rule matching model based on industry expert knowledge, a series of judgment rules are constructed, including hard matching rules (such as complete phone consistency) and soft matching rules (such as high similarity in name and close geographic location); second, a traditional machine learning model, using historical matching data, training multiple machine learning models (such as random forest, gradient boosting tree, support vector machine, etc.), learning the relationship between features and matching results; then a deep learning matching model is designed and trained (such as twin network, attention mechanism network, etc.), which is used to capture the complex nonlinear relationship between hotel and room features. Each hotel / room in the candidate matching set and the target object are scored through the three types of models respectively, and the matching scores of multiple models are obtained. Then, through weighted average, Stacking or voting and other model fusion technologies, the prediction results of multiple models are combined into the final matching score matrix. Finally, based on the matching score matrix, one-to-one matching, one-to-many matching (one new hotel corresponding to multiple hotels on the platform), many-to-one matching and other different matching scenarios are identified, and preliminary matching results are generated. This multi-model fusion method can integrate the advantages of various models to improve the accuracy and robustness of the matching.

[0096] In the multi-model fusion matching process, the topology optimization technology of sparse evolutionary neural network significantly improves the matching performance. The initial structure of the sparse MLP network contains 6 layers, and the number of neurons in each layer is [input dimension, 256, 128, 64, 32, 1], and the initial connection density is set to 30%. The genetic algorithm uses a population size of 100 individuals, a crossover probability of 0.8, a mutation probability of 0.2, and an evolution iteration of 50 generations. The fitness function is designed as 0.7*accuracy-0.3*parameter amount / baseline parameter amount to balance the accuracy and complexity. The modularization grouping uses an improved Louvain community detection algorithm, with a modularity threshold of 0.5 and a minimum module size of 3 features. For each module, the connection density is 80%, while the inter-module connection density is reduced to 10%, forming a locally dense and globally sparse network structure. The model training uses dynamic sparse training (DST) technology, pruning and growing operations are performed every 5 epochs, using the AdamW optimizer with a learning rate of 0.0005 and a weight decay of 0.01. On the test data set, the optimized sparse network reduces the parameter amount by 62% compared to the fully connected network, increases the inference speed by 3.2 times, and improves the matching accuracy by 6.5 percentage points, reaching 92.7%. Especially when dealing with heterogeneous data from different suppliers, the accuracy improves more significantly, reaching 8.9 percentage points.

[0097] Step S5: Based on the preliminary matching results and the matching score matrix, generate the final matching conclusion including matching status, matching object ID and matching confidence through Bayesian uncertainty estimation and threshold dynamic adjustment.

[0098] In this step, the preliminary matching results generated in step S4 are evaluated for confidence and decision-making to generate the final matching conclusion. Confidence evaluation first analyzes the distribution characteristics of the matching scores, including the gap between the highest score and the second highest score, the concentration of the scores, etc.; then applies the Bayesian method to estimate the uncertainty of the matching results based on historical matching data and the current matching pattern to generate a confidence index; then dynamically adjusts the matching decision threshold according to business requirements (such as preferring to miss rather than mismatch, or preferring to mismatch rather than miss) and historical matching quality; then constructs a decision matrix based on the matching scores and the confidence to divide the matching results into "definite match", "possible match", "definite mismatch" and "need manual review" four categories; finally, according to the decision matrix, generate the final matching conclusion including matching status (whether there is or not), matching object ID (such as if there is, which hotel / room type in the corresponding platform) and matching confidence (0-100% matching quantitative index). At the same time, based on feature difference analysis and the contribution of each model, generate matching basis explanation to support business personnel to understand and verify the matching results. This confidence evaluation and decision-making mechanism can quantify the reliability of the matching results, provide more basis for business decision-making, and also automatically identify complex situations that require human intervention.

[0099] In the confidence evaluation and decision-making link, the resource adaptive allocation framework based on the contrast ranking network provides an efficient computing resource management scheme. The contrast ranking network adopts a twin structure, including two sub-networks sharing parameters. Each sub-network is a 3-layer fully connected network with layer sizes of [input dimension / 2, 64, 32, 16] and uses a LeakyReLU activation function (negative slope 0.2). The network training uses a triplet loss function (Triplet Loss) with a boundary value of 0.5. Triplet training data is constructed from 1 million historical matching records, including (query hotel, correct match, and incorrect match). The training uses an SGD optimizer with an initial learning rate of 0.01 and a momentum of 0.9. The cosine annealing schedule is used, and a total of 30 epochs are trained. The resource allocation strategy divides the candidate pairs into three layers according to the prospect score: the high prospect layer (top 10% or score > 0.8), which allocates complete deep evaluation resources; the medium prospect layer (10%-40% or score between 0.5-0.8), which allocates a lightweight evaluation model; and the low prospect layer (remaining candidate pairs), which only allocates basic rule judgment. The adaptive resampling mechanism sets the accuracy drop threshold to 5%. When the accuracy of a layer drops by more than this threshold, the sampling rate of that layer is increased by 50%, and the contrast ranking network is fine-tuned using 100 randomly sampled cases with a learning rate reduced to 1 / 10 of the initial value. In actual deployment, the framework reduces the average response time for matching requests from 1.2 seconds to 0.3 seconds for a database of 100,000 hotels, reducing computing resource requirements by 73% while maintaining a matching accuracy of 91.5%, which is only 0.8 percentage points lower than full evaluation.

[0100] Step S6: Based on the final matching conclusion and the collected business feedback data, the parameters and thresholds of the matching model are optimized through online learning and regular batch training to obtain a parameter-optimized matching model.

[0101] In this step, a complete matching result feedback and model optimization mechanism is established to realize the self-learning and evolution of the system. First, the matching conclusion, key data and intermediate results in the matching process are persistently stored to establish a matching history database; then a feedback mechanism is designed and implemented to collect feedback information such as confirmation, correction or negation of the matching results by the business system; then based on the business feedback, the matching quality is continuously monitored, and indicators such as accuracy, recall rate and F1 score are calculated, and time series analysis is performed to timely detect changes in model performance; for matching results with explicit feedback, online learning methods are used to fine-tune model parameters in real time, especially feature weights and decision thresholds; when enough feedback data is collected, the matching model will also be periodically retrained in batches to update model parameters to adapt to changes in data distribution; in addition, based on matching quality analysis and error case study, the matching algorithm is continuously optimized, new features or models are introduced, and the overall matching performance is improved. This closed-loop feedback and optimization mechanism enables the system to continuously learn and evolve, and as the running time increases, the matching accuracy will continue to improve, and new matching patterns can also be quickly adapted.

[0102] Online learning and batch training optimization of the matching model are the key to the continuous evolution of the system. Online learning uses a gradient-based incremental update method, triggering an online update every 50 manually confirmed feedback data. For feature weight adjustment, the FTRL-Proximal algorithm (Follow-The-Regularized-Leader) is used, with a learning rate of 0.01, an L1 regularization coefficient of 0.1, and an L2 regularization coefficient of 0.2; for decision threshold adjustment, the Bayesian optimization method is used, using expected improvement (EI) as the acquisition function, limiting the threshold change to no more than 5% in each adjustment. Batch retraining is triggered once every 30 days or when 10,000 new feedback data are accumulated, using a sliding window strategy to retain the last 180 days of data, and assigning a weight decay coefficient of 0.8 to data over 60 days. To prevent catastrophic forgetting, the elastic weight consolidation (EWC) technique is used to set a protection coefficient for key parameters. Experiments show that through this continuous optimization mechanism, the system's matching accuracy improves from 87% initially to 94.3% after 6 months of operation, with an error rate reduction of 56%, and the ability to adapt to new online supplier data significantly improves, with a first-week matching accuracy of 88% from the previous 75%.

[0103] The above steps are described in detail as follows.

[0104] In step S1, the original information is preprocessed to generate standardized hotel and room type feature data sets, which specifically include:

[0105] Step S11: Based on the original information, perform special character removal, case unification, and missing value processing on the parsed original information to obtain the data cleaning result;

[0106] Step S12: Map the fields of the data cleaning results to a unified standard field set to generate field standardization results;

[0107] Step S13: Perform word segmentation, stop word removal, and entity recognition on the hotel name, hotel address, and room type name in the standardized field results to obtain the text structured results;

[0108] Step S14: Based on the text structuring results, obtain standardized geographic coordinates and administrative division codes through geocoding services to generate the standardized hotel and room type feature dataset.

[0109] In this embodiment, raw data reception and parsing refers to receiving the raw data of newly added hotels and room types pushed by the business system, and parsing out the basic information fields of hotels and room types according to the data formats and field definitions of different suppliers. Data cleaning and standardization refers to cleaning the parsed raw data, including removing special characters, unifying capitalization, handling missing values, correcting obvious errors, etc., and mapping the fields of different suppliers to a unified standard field set. Text information structuring processing refers to performing natural language processing such as word segmentation, stop word removal, and entity recognition on text information such as hotel names, addresses, and room type names, extracting key information and expressing it in a structured way. Geographic information standardization refers to obtaining standardized geographic coordinates (latitude and longitude) and administrative division codes based on hotel address information through geocoding services, and calculating the relative positional relationship with surrounding landmarks. Feature data completion refers to completing key missing fields based on existing information through rule inference, external API calls, etc., such as inferring administrative divisions from addresses and inferring brands from hotel names. Data quality assessment refers to evaluating the quality of the cleaned and standardized data, calculating the completeness, consistency, and reliability scores of data in each dimension, and providing a basis for subsequent feature weight allocation.

[0110] Please see Figure 2 , Figure 2 A flowchart illustrating the construction of multi-level feature vectors provided in embodiments of the present invention. Figure 2 As shown, in step S2, feature engineering is used to construct basic feature vectors, semantic feature vectors, and geospatial feature vectors to obtain multi-level feature vectors, specifically including:

[0111] Step S21: Based on the standardized hotel and room type feature dataset, extract hotel name, hotel star rating, hotel address, room type name, room area, and bed type features to construct a basic feature vector.

[0112] In this step, based on the standardized data processed in step S1, the basic features of hotels and room types are extracted, and the basic feature vectors are constructed. For hotel features, the hotel name (which can be further divided into brand name, geographical location words, type words, etc.), star rating (such as five-star, four-star, etc.), hotel address (which can be further divided into province, city, district, street, etc.), contact number, opening time, decoration time, total number of rooms, hotel type (such as business hotel, holiday hotel, etc.), facility list (such as swimming pool, gym, conference room, etc.), service list (such as pick-up service, luggage storage, etc.), and other basic information are extracted. For room type features, the room type name (which can be further divided into room type, bed type, view, etc.), room area, bed type configuration (such as double bed, twin bed, etc.), maximum number of occupants, floor information, whether there is a window, bathroom facilities, entertainment facilities, network facilities, and other basic information are extracted. These features are usually structured and can be directly used for comparison and matching. Different types of features will be represented differently, such as numerical features (such as area, star rating, etc.) keeping the original numerical values, categorical features (such as facility list, service list, etc.) converted to one-hot encoding or multi-hot encoding, and text features (such as name, address, etc.) may need further processing. By constructing the basic feature vector, the basic attributes of hotels and room types can be captured, providing a direct basis for comparison for subsequent matching.

[0113] Step S22: For the text information in the standardized hotel and room type feature dataset, a pre-trained language model is used for conversion to generate semantic feature vectors.

[0114] In this step, the text information of hotels and room types is converted into high-dimensional semantic vectors using pre-trained language models to capture deep semantic information of the text. First, select a pre-trained language model suitable for Chinese processing, such as BERT (Bidirectional Encoder Representations from Transformers), Word2Vec, FastText, etc. These models have learned rich semantic knowledge through pre-training on large-scale corpora. Then, input the text information of hotel names, hotel descriptions, room type names, room type descriptions, etc. into the pre-trained model to obtain the corresponding semantic representation. For example, for the BERT model, the output of the [CLS] token can be extracted as the semantic representation of the entire text; for the Word2Vec model, the weighted average of all word vectors can be calculated as the semantic representation of the text. These semantic vectors can capture the deep semantic information of the text, and even if the expression is different, as long as the semantics are similar, the vectors will also be similar. For example, "sea view double bed room" and "ocean-facing super king room" may have different expressions, but their semantics are similar, and their semantic vectors will also be similar. In addition, special terms and expressions in the hotel field will be fine-tuned for the pre-trained model or domain-specific word embeddings will be constructed to improve the accuracy of semantic representation. By generating semantic feature vectors, it is possible to go beyond superficial text matching and achieve semantic-based deep matching, effectively handling different suppliers using different expressions.

[0115] Step S23: Based on the standardized hotel and room type feature data set, construct a geographic spatial feature vector including latitude and longitude coordinates and administrative division codes.

[0116] In this step, based on standardized geographic information, a geospatial feature vector is constructed for accurate matching and comparison of geographic locations. First, latitude and longitude coordinates are taken as basic geographic features, which are the most accurate representation of geographic location; then, administrative division codes (such as standard codes for countries, provinces, cities, and districts) are taken as hierarchical geographic features, which help quickly filter hotels in different regions; next, the distance and orientation of the hotel to major landmarks (such as airports, train stations, business centers, tourist attractions, etc.) are calculated, which are also important for hotel matching, as the relative positional relationship with landmarks is usually consistent in different suppliers' descriptions of the same hotel, although the address may vary slightly; in addition, regional type information such as the commercial district, residential area, or tourist area where the hotel is located is also extracted, which helps to distinguish hotels with the same name in different areas of the city. In order to facilitate similarity calculation, special processing is performed on geographic coordinates, such as converting latitude and longitude to Mercator projection coordinates or calculating Geohash codes. By constructing a geospatial feature vector, the geographic location of a hotel can be accurately compared, which is one of the key bases for hotel matching, as the geographic location of the same hotel must be very close.

[0117] Step S24: Weight fusion and dimension reduction processing is performed on the basic feature vector, the semantic feature vector, and the geospatial feature vector to generate the multi-level feature vector.

[0118] In this step, the basic feature vector, semantic feature vector, and geospatial feature vector constructed earlier are fused to generate a comprehensive multi-level feature vector. First, based on historical matching data and domain knowledge, the importance of each feature dimension is evaluated. For example, by analyzing historical matching data, it may be found that the semantic similarity of hotel names and the proximity of geographic locations have the greatest impact on matching results, while the difference in facility lists has less impact. Based on this evaluation, the system assigns different weight coefficients to different features. Then, the feature vectors are fused according to the weights, which can be done using simple weighted summation or more complex nonlinear fusion methods. Since the dimension of the fused feature vector may be very high (possibly hundreds or thousands of dimensions), the system will perform dimension reduction using techniques such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), or autoencoder to retain the most important information while reducing computational complexity. In addition, the features are normalized to ensure comparability of different dimensions. The final multi-level feature vector contains not only basic attribute information of hotels and room types, but also deep semantic information of text and accurate geographic location information, providing a comprehensive and accurate feature representation for subsequent matching.

[0119] In this embodiment, the basic feature vector construction refers to extracting the basic features of hotels and room types based on the standardized data processed in step S1, including hotel name, star rating, address, phone number, facility list, etc., and room type name, area, bed type, capacity, etc., to construct the basic feature vector. The semantic feature vector generation refers to using a pre-trained language model (such as BERT, Word2Vec, etc.) to convert text information such as hotel name, room type name, and hotel description into high-dimensional semantic vectors to capture the semantic information of the text. The geospatial feature vector construction refers to constructing geospatial feature vectors based on standardized geographic information, including latitude and longitude coordinates, administrative division codes, distances to major landmarks, etc. The business feature vector extraction refers to extracting features from a business perspective, such as hotel price range, rating, room type price range, and promotion strategy, to construct a business feature vector. The feature importance evaluation and weight assignment refers to evaluating the importance of each feature dimension based on historical matching data and domain knowledge, and assigning reasonable weight coefficients. The feature vector fusion and dimension reduction refer to fusing multi-dimensional feature vectors according to weights and performing dimension reduction through principal component analysis (PCA) or t-SNE technology to generate the final comprehensive feature vector.

[0120] In a preferred implementation of this embodiment, generating the multi-level feature vector further includes frequency domain transformation-based feature representation learning, specifically including:

[0121] Step S25: Discrete Fourier transform is performed on the standardized hotel and room type feature dataset to convert multi-dimensional data from time domain to frequency domain, obtaining frequency domain representation data.

[0122] In this step, an innovative frequency domain analysis method is introduced, treating the multi-dimensional feature data of hotels and room types as signals and converting them from time or spatial domain to frequency domain space through Discrete Fourier Transform (DFT). The core idea of this method is to decompose complex hotel feature patterns into different frequency components, enabling more effective capture of periodic patterns and essential features in the data. For different types of hotel features, the system adopts different frequency domain transformation strategies: for discrete features such as hotel facility lists, the system treats them as high-dimensional binary signals and applies multi-dimensional DFT for transformation; for geographic coordinate information, the system applies two-dimensional DFT to capture spatial distribution patterns; for price time series data, the system applies one-dimensional DFT to analyze its periodic variation rules. In actual implementation, appropriate transformation window size and sampling frequency are selected according to the data characteristics of different features to ensure that the frequency domain representation after transformation accurately reflects the characteristics of the original data. Through frequency domain transformation, the system can decompose complex patterns in the original feature data into frequency components, where low-frequency components usually represent the main structure or overall trend of the data, and high-frequency components usually correspond to details or noise. This decomposition enables the system to more targetedly focus on the most discriminative frequency intervals, filter out non-standardized expression noise in different supplier descriptions, and retain the essential features of hotels.

[0123] Step S26: Based on the frequency domain representation data, feature compression and reconstruction are performed through a frequency domain autoencoder network to obtain frequency domain feature representation.

[0124] In this step, an innovative frequency domain autoencoder network is designed and trained to further learn and compress the frequency domain representation data generated in step S25. Unlike traditional autoencoders that directly reconstruct the original input, this network takes standardized feature data of hotels as input but reconstructs the frequency domain representation as the target. The network architecture includes an encoder and a decoder: the encoder part is composed of multiple layers of neural networks, responsible for compressing the original features into a low-dimensional latent space; the decoder part attempts to reconstruct the frequency domain representation of the input data, especially the low-frequency components with high amplitude. In the training process, a specially designed loss function is used to assign different weights to different frequency components, with the weight of low-frequency components (representing the main structure) higher than that of high-frequency components (representing details or noise). This design forces the network to retain information in the latent space that can reconstruct key frequency components, which are more likely to reflect the essential features of hotels. In addition, regularization techniques such as sparsity constraints or variational regularization are introduced to further improve the quality and generalization ability of the latent representation. Through the training of the frequency domain autoencoder network, a compact and expressive feature representation can be learned, which effectively captures the unique patterns of different types of hotels in the frequency domain space, providing a more robust feature basis for subsequent matching.

[0125] Step S27: Perform spectral energy distribution analysis on the frequency domain feature representation to determine the key frequency interval, and obtain the optimized frequency domain feature.

[0126] In this step, the frequency domain feature representation generated in step S26 is subjected to in-depth spectral energy distribution analysis to determine the key frequency interval with the highest discriminative power. First, the energy distribution of the frequency domain feature in each frequency interval is calculated, usually by calculating the power spectral density or spectral energy cumulative distribution. Then, the spectral energy distribution characteristics of different types of hotels (such as business hotels, holiday hotels, and economy hotels) are analyzed to identify the frequency interval that can effectively distinguish different types of hotels. Through extensive experiments and statistical analysis, it is found that different types of hotels exhibit unique patterns in specific frequency intervals, for example, business hotels and holiday hotels show significant differences in the spectral characteristics of price fluctuations, holiday hotels usually exhibit stronger seasonal fluctuations (corresponding to specific low-frequency components), while business hotels are relatively stable. Based on these findings, a frequency selection filter is designed to retain the most discriminative frequency components and suppress or filter out irrelevant frequency components. Experiments show that using only 30% of the total spectrum of low-frequency components for training can achieve a matching accuracy of more than 95% using full-spectrum training, while reducing the computational complexity by about 60%. In particular, when dealing with small supplier data that describes text with extreme irregularities, this optimized frequency domain feature shows stronger robustness, with a matching accuracy improvement of more than 15 percentage points. Through spectral energy distribution analysis and determination of the key frequency interval, the system can generate more concise and effective frequency domain feature representation.

[0127] Step S28: Fuse the optimized frequency domain feature with the base feature vector, the semantic feature vector, and the geospatial feature vector to generate an enhanced multi-level feature vector.

[0128] In this step, the optimized frequency domain features generated in step S27 are intelligently fused with the previously constructed base feature vector, semantic feature vector, and geospatial feature vector to generate a more comprehensive and powerful multi-level feature representation. This fusion is based on the recognition that frequency domain features are complementary to other features, with frequency domain features being good at capturing global patterns and essential characteristics, while the base feature vector and semantic feature vector focus more on local details and semantic information, and the geospatial feature provides precise location information. A multi-level feature fusion strategy is adopted: first, normalize each type of feature to ensure comparability of features from different sources; then, learn the correlation and complementarity between different features, which can be dynamically adjusted by attention mechanisms or gating units to adjust the weights of different features; then, use a nonlinear fusion method (such as a multi-layer perceptron or convolutional neural network) to deeply fuse different features to generate a unified representation; finally, residual connections or skip connections may be applied to ensure that the original feature information is not lost in the fusion process. In actual application, when processing the newly added hotel data named "Seaview Bay Resort Hotel", the traditional method may directly compare its name, address, etc. original features, while using enhanced multi-level features, the system can consider the comprehensive performance of the hotel in name semantics, geographic location, facility configuration, and price seasonal fluctuations, etc. frequency domain features, even if there are large differences in the description text of different suppliers, the same hotel can be accurately identified. Through this multi-level feature fusion, the system can build a more comprehensive, robust, and expressive feature representation, significantly improving the matching accuracy in the cross-supplier data scenario.

[0129] In this embodiment, the core idea of feature representation learning based on frequency domain transformation is to regard the multi-dimensional data of hotel descriptions as a signal, and convert it from time / space domain to frequency domain through techniques such as Discrete Fourier Transform (DFT), and learn the feature representation in the frequency domain space. The advantage of this method is that it can separate the high-frequency components (usually representing noise or details) and low-frequency components (usually representing the main structure or pattern) in the data, allowing the system to focus on the most discriminative frequency intervals.

[0130] In specific implementation, a frequency domain autoencoder network structure is designed. Unlike traditional autoencoders that directly reconstruct the original input, this network takes the standardized feature data of the hotel as input, but takes its frequency domain representation as the reconstruction target. The encoder part is responsible for compressing the original features into the latent space, while the decoder tries to reconstruct the frequency domain representation of the input data, especially those with high amplitude low-frequency components. This design forces the network to retain information in the latent space that can reconstruct key frequency components, which are often more reflective of the essential characteristics of the hotel.

[0131] Different frequency domain transformation strategies are adopted for different types of hotel features. For example, for discrete features such as hotel facility lists, they are transformed as high-dimensional binary signals; for geographic coordinate information, two-dimensional DFT is applied to capture spatial distribution patterns; for price time series data, one-dimensional DFT is applied to analyze its periodic variation rules. Through frequency domain analysis, it is found that different types of hotels show unique patterns in certain frequency intervals, such as business hotels and holiday hotels, which have obvious differences in the frequency spectrum characteristics of price fluctuations.

[0132] In practical applications, when processing the newly added hotel data named "Sea View Holiday Hotel", the traditional method may directly compare its name, address and other original features. However, after using frequency domain feature representation, it can identify the similarity of this hotel in price seasonal fluctuation, facility configuration mode and other frequency domain features with other holiday hotels, even if their direct description text may differ greatly. More importantly, frequency domain analysis can filter out non-standardized expression noise in different supplier descriptions and retain the essential features of the hotel.

[0133] Through a large number of experiments, it is found that using only 30% of the low-frequency components of the total frequency spectrum for training can achieve more than 95% of the matching accuracy of using the full frequency spectrum for training, while reducing the computational complexity by about 60%. Especially when dealing with small supplier data with extremely non-standardized description text, frequency domain feature representation shows stronger robustness, with a matching accuracy improvement of more than 15 percentage points.

[0134] Frequency domain feature representation is complementary to other feature vectors in step S2. Frequency domain features are good at capturing global patterns and essential features, while the original basic feature vectors and semantic feature vectors focus more on local details and semantic information. Through the feature fusion layer, these different dimensional representations are integrated to generate a more comprehensive hotel feature representation.

[0135] The following technical points need to be paid attention to in the implementation process: (1) Select appropriate frequency domain transformation method for different types of features; (2) Determine the key frequency interval according to the frequency spectrum energy distribution; (3) Design an effective frequency domain reconstruction loss function to balance the weights of low and high frequency components; (4) Build a fusion mechanism for frequency domain features and other features to ensure information complementarity rather than redundancy.

[0136] By introducing frequency domain transformation-based feature representation learning, the hotel room type matching system can understand and compare hotel features from a new dimension, effectively overcoming the noise and non-standardized expression problems in the original data, significantly improving the matching accuracy and system robustness in the cross-supplier data scenario, and providing strong technical support for the platform to integrate multi-source hotel resources.

[0137] The combination of the inverted index and the locality-sensitive hashing algorithm in step S3 filters from the platform full hotel and room type data to generate a candidate matching set, specifically including:

[0138] Step S31: Based on the multi-level feature vector, an inverted index based on hotel name and hotel address is constructed, and a candidate hotel subset is obtained through fast filtering of the city, administrative division, and star rating attributes of the hotel.

[0139] In this step, an efficient index structure is first constructed to support fast retrieval of potential matching items from massive hotel data. The system constructs a multi-level inverted index and a geographic spatial index based on platform full hotel and room type data (which may contain hundreds of thousands or even millions of records). For hotel names, the system establishes an inverted index after word segmentation, enabling hotels containing specific words to be quickly retrieved. For hotel addresses, the system also establishes an inverted index based on address keywords. For geographic location, a geographic spatial index (such as R-tree, quadtree, etc.) is constructed to support fast queries based on geographic ranges. After the index is constructed, coarse-grained fast filtering is performed: first, filter based on the city where the hotel is located, which is the most basic screening condition, as different cities with the same name usually are not the same hotel. Then, further filter based on the administrative division (such as district and county) to narrow the search range. Then, filter based on key attributes such as star rating, as the star rating of the same hotel on different suppliers is usually the same or similar. This multi-level coarse-grained filtering can quickly reduce the search space from full data to a smaller candidate subset, usually reducing the data volume by more than 99%, greatly improving the efficiency of subsequent fine matching. For example, for a "four-star Hilton Hotel" located in "Chaoyang District, Beijing", it can quickly locate all four-star hotels in Chaoyang District, Beijing, reducing the candidate set from hundreds of thousands of hotels nationwide to dozens of hotels, significantly reducing the computational complexity of subsequent processing.

[0140] Step S32: Apply the locality-sensitive hashing algorithm to the multi-level feature vector to map to a low-dimensional hash code and find a set of potential similar objects.

[0141] In this step, the multi-level feature vector constructed in step S2 is applied with a local sensitive hashing (LSH) algorithm to map the high-dimensional feature vector to a low-dimensional hash code, realizing fast retrieval of similar hotels. The core idea of local sensitive hashing is to design a special hash function so that similar objects in the original feature space are mapped to the same or similar hash buckets, while dissimilar objects are mapped to different hash buckets. According to the characteristics of different types of features, select the appropriate LSH algorithm: for the basic feature vector and the semantic feature vector, MinHash or SimHash algorithm may be used; for the geographical space feature vector, Geohash or Grid-based LSH algorithm may be used. In order to improve the recall rate of the query, usually multiple independent LSH indexes (such as 10-20) are constructed, each index uses a different hash function family, and then the query results are combined. When querying, the feature vector of the new hotel is converted into the corresponding hash code, and then the hotels with the same or similar hash code are found in each LSH index, which are likely to be similar to the query hotel. Compared with the traditional brute force search, LSH can complete the approximate nearest neighbor search in sub-linear time, greatly improving the retrieval efficiency. For example, in a database containing 1 million hotels, the traditional method needs to calculate the similarity between the query vector and all hotel vectors, while LSH may only need to calculate the similarity with a few hundred or a few thousand candidate hotels, while still maintaining a high recall rate (usually above 90%). Through the LSH algorithm, potential similar hotels and room types can be quickly found, further narrowing the range of the candidate matching set.

[0142] Step S33: Calculate the name similarity, geographical location similarity and facility similarity for the set of potential similar objects to obtain multi-dimensional similarity indicators.

[0143] In this step, multi-dimensional fine similarity calculation is performed on the set of potential similar objects selected by the LSH algorithm to more accurately assess the similarity between hotels. First, name similarity is calculated. Various text similarity algorithms can be used, such as Levenshtein distance to measure the difference between two strings; Jaccard coefficient to measure the overlap of word sets; cosine similarity to compare the direction similarity of word vectors; in addition, semantic similarity of hotel names is also considered, which captures the semantic consistency under different expressions by comparing the semantic vectors of the names. Second, geographic location similarity is calculated, mainly based on geographic distance (such as Euclidean distance, Haversine distance, etc.) to measure the physical distance between two hotels. Generally, hotels within 100 meters are considered to be the same hotel. Then, facility similarity is calculated, which assesses the similarity of two hotels by comparing the overlap of their facility lists. Jaccard coefficient or weighted Jaccard coefficient (considering the importance of different facilities) can be used. In addition, other dimensions of similarity are calculated, such as star similarity, price interval similarity, customer rating similarity, etc. For each similarity dimension, reasonable threshold and weight are set according to historical matching data and domain knowledge to balance precision and recall. Through multi-dimensional similarity calculation, the similarity between candidate hotels and target hotels can be comprehensively evaluated, providing a basis for subsequent sorting and screening.

[0144] Step S34: Sorting and truncation based on the weighted score of the multi-dimensional similarity index to generate the candidate matching set.

[0145] In this step, based on the multi-dimensional similarity indicators calculated in step S33, a comprehensive similarity score is generated by weighted fusion, and the candidate hotels are sorted and screened accordingly, and finally a candidate matching set is generated. First, weights are assigned to the similarity indicators of different dimensions, which can be automatically learned by machine learning algorithms based on historical matching data, or manually set based on domain expert knowledge. Generally, the weights of geographical location similarity and name similarity are higher, because these two dimensions are the most critical for determining whether it is the same hotel. Then, the weighted comprehensive score is calculated, which can use simple linear weighting or more complex nonlinear fusion methods. Then, the candidate hotels are sorted in descending order according to the comprehensive score, and the higher the score, the more likely it is a matching hotel. Finally, the sorting results are truncated, only keeping the top N candidate hotels (N is usually 10-50, depending on the calculation resources of subsequent fine matching), which constitute the final candidate matching set. In the truncation process, the score distribution is also considered, if the scores of the top N are significantly higher than other candidates, only the N high-score candidates may be retained; if the score distribution is relatively flat, the system may retain more candidates to improve recall. In addition, feature difference analysis is also performed on each hotel in the candidate matching set and the target hotel, recording their specific differences in each dimension, providing detailed judgment basis for subsequent fine matching. Through this multi-dimensional weighted sorting and intelligent truncation strategy, a candidate matching set of moderate size and high quality can be generated, which not only guarantees the efficiency of subsequent fine matching, but also maintains a high recall rate.

[0146] In this embodiment, index system construction and maintenance refers to constructing multi-level inverted index and geographic spatial index based on platform full hotel and room type data, supporting efficient similarity retrieval. Coarse-grained fast filtering refers to quickly filtering out a subset of possible matching hotels based on key attributes such as hotel city, administrative division, star rating, etc., reducing the search space. Key feature local sensitive hashing refers to applying local sensitive hashing (LSH) algorithm to the comprehensive feature vector constructed in step S2, mapping high-dimensional vector to low-dimensional hash code, and quickly finding potential similar hotels and room types. Multi-strategy similarity calculation refers to calculating multi-dimensional similarity indicators such as name similarity (using edit distance, Jaccard coefficient, etc.), geographical location similarity (based on geographical distance), facility similarity, etc. for the filtered candidate set. Candidate sorting and truncation refers to sorting the candidate matching objects based on the weighted score of the multi-dimensional similarity indicators, and selecting the top N as the candidate matching set to control the computational complexity of subsequent fine matching. Feature difference analysis refers to analyzing the feature difference between each hotel / room type in the candidate matching set and the target object, providing judgment basis for subsequent fine matching.

[0147] The matching model is constructed by a rule matching model, a machine learning model and a deep learning model in step S4, and then a preliminary matching result is generated by fusing and calculating a matching score matrix, specifically including:

[0148] Step S41: Based on the candidate matching set, scoring is performed by hard matching rules and soft matching rules to obtain a rule matching score.

[0149] In this step, based on industry expert knowledge and experience, a series of judgment rules are constructed to score each hotel in the candidate matching set. These rules are divided into two categories: hard matching rules and soft matching rules. Hard matching rules usually have high certainty, and hotels that meet these rules are very likely to be matched, such as completely consistent phone numbers (considering that the phone number is one of the unique identifiers of the hotel), completely consistent hotel names and very close geographic locations (such as a distance of less than 50 meters), completely consistent hotel official addresses, etc. These rules are usually implemented using Boolean logic, and the result is "yes" or "no". Soft matching rules are more flexible, and hotels that meet these rules have a certain possibility of being matched, but need to be combined with other evidence, such as highly similar hotel names (such as an edit distance less than a threshold) and close geographic locations (such as a distance less than 500 meters), highly overlapping hotel facility lists (such as a Jaccard coefficient greater than a threshold), consistent hotel star ratings and the same business district, etc. These rules usually return a continuous score between 0 and 1, indicating the likelihood of matching. The system assigns different weights to different rules, and the weight of hard rules is usually higher than that of soft rules. In the scoring process, the system will consider the results of multiple rules to calculate the weighted score. For example, for a newly added hotel named "Beijing Wangfujing XXX Hotel", if there is a hotel named "Beijing Wangfujing XXX Hotel" with an address of "Beijing Dongcheng District, Wangfujing East Street 1" in the candidate set, and the address of the newly added hotel is "Beijing Dongcheng District, Wangfujing East Street 1", and the phone numbers are also completely consistent, then this pair of hotels will get a very high score under the hard rule. The advantage of the rule matching model is strong interpretability, simple implementation, and can effectively handle cases with clear matching characteristics.

[0150] Step S42: Scoring is performed on the candidate matching set using random forest, gradient boosting tree and support vector machine models to obtain a machine learning matching score.

[0151] In this step, the candidate matching set is scored using traditional machine learning models that can learn complex non-linear relationships between features and matching results. First, training data is prepared, including positive examples (confirmed pairs of the same hotel) and negative examples (confirmed pairs of different hotels) from historical matches. For each pair of hotels, multiple features are extracted, such as name similarity, geographic distance, facility overlap, star level difference, etc. Then, multiple machine learning models are trained: a random forest model, which builds multiple decision trees and takes their average prediction results, has good generalization ability and robustness to noise; a gradient boosting tree model (such as XGBoost, LightGBM, etc.), which iteratively trains weak learners and combines them, can capture complex interactions between features; a support vector machine model, which separates positive and negative samples by finding the maximum margin hyperplane, performs well in high-dimensional feature space. These models have their own advantages: random forest is good at resisting overfitting and suitable for noisy data; gradient boosting tree usually performs best in accuracy but may require more detailed parameter adjustment; support vector machine still performs well when the number of features is higher than the number of samples. In the training process, techniques such as cross-validation are used to adjust model parameters to ensure the generalization ability of the model. For new matching tasks, the features of the candidate matching pairs are input into the trained model to obtain the matching probability or score. For example, when evaluating the matching degree of "Beijing Wangfujing Hilton Hotel" and the candidate hotel, the random forest model may pay special attention to the geographic location and hotel name similarity, while the gradient boosting tree model may find that the combination of star level and facility list is important for matching judgment. Through these traditional machine learning models, complex relationships between features and matching results can be learned, improving the accuracy of matching.

[0152] Step S43: Score the candidate matching set using a twin network and an attention mechanism network to obtain a deep learning matching score.

[0153] In this step, deep learning models are designed and trained to capture complex non-linear relationships between hotel and room type features, especially those patterns that are hard to capture by hand-crafted rules or traditional machine learning models. Two types of deep learning architectures are mainly adopted: Siamese networks and attention mechanism networks. Siamese networks consist of two sub-networks that share parameters, processing features of a pair of hotels respectively, and then comparing their representations through distance functions or similarity functions. This architecture is particularly suitable for similarity judgment tasks, and can learn a metric space where different descriptions of the same hotel are mapped to close points, while different hotels are mapped to distant points. Specific implementations that the system can use include Siamese CNNs (for processing text or image features), Siamese LSTMs (for processing sequence features), etc. Attention mechanism networks can automatically learn the importance weights of different features and dynamically adjust these weights in different matching scenarios. For example, for some hotels, the name may be the most important matching basis; while for other hotels, the geographic location may be more critical. Attention mechanisms can adaptively focus on the most relevant features according to specific inputs. Possible implementations include self-attention mechanisms, cross-attention mechanisms, etc. During training, special designed loss functions such as contrastive loss or triplet loss are used to make the model learn more discriminative feature representations. In addition, transfer learning techniques are also used to initialize network parameters using pre-trained models on large-scale general-purpose datasets (such as BERT, ResNet, etc.), and then fine-tune them on hotel matching data, which can achieve good performance on smaller training datasets. Through these deep learning models, more complex matching patterns can be captured, especially when dealing with large differences in text descriptions, varying image quality, or incomplete features, showing obvious advantages.

[0154] Step S44: Perform weighted average fusion on the rule matching score, the machine learning matching score, and the deep learning matching score to generate the matching score matrix and the preliminary matching result.

[0155] In this step, the matching scores of different models generated in the previous three steps are intelligently fused to combine the advantages of each model and generate the final matching score matrix and preliminary matching results. The system uses multiple model fusion techniques: the simplest is weighted average, which assigns a weight to each model. The weight can be determined based on the model's performance on the validation set. Generally, deep learning models have higher weights, but rule-based models are also important in certain explicit scenarios. The more complex is the Stacking method, which trains a meta-model using the prediction results of each base model as features to learn how to optimally combine these predictions. In addition, voting mechanisms can also be used, especially for binary classification decisions (match or not match), where majority voting or weighted voting can be used. During the fusion process, the system considers the performance differences of different models in different scenarios. For example, rule-based models perform well in cases with explicit matching features. Machine learning models perform stably when features are complete and distributed stably. Deep learning models have obvious advantages in handling complex text or incomplete features. The weights of different models can be dynamically adjusted based on the characteristics of the input features. After fusion, a matching score matrix is generated, where each element represents the comprehensive matching score of a pair of candidate matching hotels. Based on this score matrix, the system further analyzes the matching patterns and identifies different matching scenarios such as one-to-one matching (one new hotel corresponds to one hotel on the platform), one-to-many matching (one new hotel corresponds to multiple hotels on the platform, possibly due to repeated entry), and many-to-one matching (multiple new hotels correspond to the same hotel on the platform, possibly due to data from different suppliers), and generates preliminary matching results. For example, if a new hotel has a much higher matching score with a certain hotel on the platform than with other candidates, the system will determine it as a one-to-one match. If the scores with multiple hotels are all high, it may be a one-to-many match, which requires further analysis or manual review. Through this multi-model fusion approach, the system can take advantage of the strengths of different models and improve the accuracy and robustness of matching.

[0156] In this embodiment, rule matching model construction refers to constructing a series of judgment rules based on industry expert knowledge, including hard matching rules (such as complete phone consistency) and soft matching rules (such as high similarity in name and close geographic location). Traditional machine learning model training refers to using historical matching data to train various machine learning models (such as random forest, gradient boosting tree, support vector machine, etc.) to learn the relationship between features and matching results. Deep learning matching model construction refers to designing and training deep learning models (such as twin networks, attention mechanism networks, etc.) to capture the complex nonlinear relationship between hotel and room features. Model prediction and scoring refers to scoring each hotel / room in the candidate matching set and the target object through rule models, machine learning models, and deep learning models, respectively, to obtain matching scores for multiple models. Model fusion and comprehensive scoring refers to combining the prediction results of multiple models into a final matching score matrix through weighted averaging, Stacking, or voting model fusion techniques. Matching pattern recognition refers to identifying one-to-one matching, one-to-many matching (one new hotel corresponding to multiple hotels on the platform), and many-to-one matching based on matching score patterns, and generating preliminary matching results.

[0157] In a preferred implementation of this embodiment, generating the preliminary matching results further includes topology optimization of the sparse evolutionary neural network, specifically including:

[0158] Step S45: Based on the candidate matching set, a sparse MLP network structure is constructed, a topology search space including the number of layers, the number of neurons, and the connection mode is defined, and an initial network topology is obtained.

[0159] In this step, the topology optimization technique of sparse evolutionary training is introduced, aiming to solve the problems of parameter redundancy, waste of computing resources and difficulty in adapting to complex matching scenarios in traditional deep learning models. The system first constructs a sparse multi-layer perceptron (Sparse MLP) network structure. Unlike traditional fully connected networks, sparse MLP allows connections in the network to be sparse, that is, not every neuron is connected to all neurons in the next layer. This sparse connection mode can greatly reduce the number of parameters while maintaining the model's expressive ability. A topology search space containing multiple dimensions is defined: the number of layers dimension, with a possible value range from 3 to 10 layers; the number of neurons per layer dimension, with a possible value range from tens to hundreds; the connection mode dimension, including full connection, local connection, jump connection and other different modes. In the initialization stage, several strategies can be used: random initialization, randomly sampling a topology structure in the search space; domain knowledge-based initialization, designing an initial topology according to the characteristics of the hotel matching task, such as focusing on basic feature extraction in the first few layers, feature interaction in the middle layers, and decision-making in the last few layers; based on existing models initialization, modifying the initial topology from existing successful network architectures (such as ResNet, DenseNet, etc.). The initial topology structure is usually over-parameterized, that is, it contains more layers and neurons than the final required, which provides sufficient search space for subsequent evolutionary optimization. Some constraints are also set, such as minimum and maximum number of layers, minimum and maximum number of neurons per layer, minimum connection density, etc., to ensure that the generated network structure is within the range of practical feasibility. In this way, the system obtains an initial network topology structure as the starting point for subsequent evolutionary optimization.

[0160] Step S46: For the initial network topology, find the optimal topology structure in the search space through a genetic algorithm, evaluate using a fitness function that combines matching accuracy and model complexity, and obtain the optimized network topology.

[0161] In this step, the initial network topology generated in step S45 is evolved and optimized using a genetic algorithm to find the most suitable network structure for the hotel matching task. Genetic algorithms are optimization algorithms inspired by biological evolution, which search for the optimal solution in the search space by simulating mechanisms such as natural selection, crossover, and mutation. First, a fitness function is defined to evaluate the goodness of a network topology, which considers multiple objectives: matching accuracy, usually measured by F1 score or AUC value on the validation set; model complexity, usually measured by the number of parameters, FLOPs (floating-point operations), or inference time; sometimes also considering the interpretability of the model or performance on specific hardware. The fitness function is usually a weighted combination of these objectives, with weights reflecting the relative importance of different objectives. Then, the evolution process begins: initialize the population, containing multiple different network topologies, possibly including the initial topology generated in step S45 and its variants; evaluate the fitness of each topology in the population; select topologies with high fitness as parents; generate child topologies through crossover operations, for example, exchanging certain layers or connection patterns of two parent topologies; perform mutation operations on child topologies, randomly changing the number of neurons or connection patterns of certain layers; evaluate the fitness of newly generated child topologies; update the population, retaining topologies with high fitness and eliminating those with low fitness. This evolution process iterates for multiple rounds (usually tens to hundreds of rounds) until a topology structure that meets the requirements is found or the pre-set number of iterations is reached. During the evolution process, the system may employ some techniques to improve efficiency, such as early stopping strategy (stopping if there is no improvement for consecutive rounds), elite preservation strategy (ensuring that the best topology is not lost during the evolution process), etc. Finally, an optimized network topology structure is obtained, which balances the matching accuracy and model complexity well.

[0162] Step S47: Based on the optimized network topology, analyze the interdependence between features, group highly correlated features into the same module, and form a modularized grouping structure.

[0163] In this step, the Modular Ordering technique is introduced. By analyzing the interdependence between features, highly correlated features are grouped into the same module, forming a locally dense but globally sparse connection pattern. This modular grouping is the key mechanism for sparse MLP optimization, which can significantly reduce the number of parameters and computational complexity while maintaining the model's expressive ability. The system first performs a correlation analysis on the features in the hotel matching task, calculating the mutual information, Pearson correlation coefficient or other correlation measures between features. Based on these correlation measures, the system constructs a feature dependency graph, where nodes represent features and edges represent the dependency between features, and the weight of the edge represents the dependency strength. Then, apply community detection algorithms (such as Louvain algorithm, spectral clustering, etc.) to identify feature modules on the feature dependency graph, grouping highly correlated features into the same module. In the hotel matching scenario, these modules usually have clear semantic meanings, such as: basic information module, including hotel name, address, phone number, etc. basic information; geographic location module, including latitude and longitude coordinates, administrative division, surrounding landmarks, etc. geographic information; facility service module, including the presence or rating of various facilities and services; price evaluation module, including price range, customer rating, evaluation text, etc. The system will allocate different resources (such as the number of neurons) to different modules according to the semantic meaning and importance of the module. In addition, the system will analyze the dependency between modules to determine the connection pattern between modules. For example, the basic information module and the geographic location module may require more connections because they jointly determine the uniqueness of the hotel; while the facility service module and the price evaluation module may require fewer direct connections. Through this modular grouping, a network structure that better meets the characteristics of the hotel matching task can be constructed, ensuring sufficient interaction between key features while avoiding unnecessary connections that waste computational resources.

[0164] Step S48: Train the network using the locally dense and globally sparse connection pattern of the modular grouping structure to obtain a sparse evolutionary matching model.

[0165] In this step, based on the modularization grouping structure formed in step S47, a neural network with local dense but globally sparse connection patterns is constructed and trained. This special connection pattern is the core feature of the sparse evolutionary matching model. The system first designs the network architecture according to the modularization grouping structure: dense connections are used within modules to ensure that features within the same module can interact fully and capture complex nonlinear relationships; sparse connections are used between modules to retain only necessary cross-module connections, reducing the number of parameters and computational complexity; at the same time, skip connections or residual connections may be introduced to allow information to be directly transmitted between different layers, alleviating the problem of gradient vanishing and improving training efficiency. Then, the training data is prepared, including positive and negative examples of historical matches, each sample containing a pair of hotel feature vectors and matching labels. During training, special optimization strategies are used: first, sparse regularization, through L1 regularization or other sparse promotion techniques, to encourage the model to learn more sparse weights; second, dynamic pruning, which periodically removes connections with weights close to zero, further improving the sparsity of the model; in addition, knowledge distillation techniques may be used to use a pre-trained large model (such as a fully connected network) to guide the training of the sparse model, improving training efficiency and model performance. After training, the model is evaluated and fine-tuned: the matching accuracy and computational efficiency of the model are evaluated on the validation set; if the performance does not meet the requirements, the module division or connection pattern may be adjusted and then retrained; if the model is too sparse and lacks expression ability, the number of connections may be appropriately increased; if the model is still too complex, further pruning or quantization may be performed. Through this local dense and globally sparse training strategy, the system ultimately obtains a sparse evolutionary matching model that maintains high matching accuracy and has lower computational complexity.

[0166] Step S49: Score the candidate matching set using the sparse evolutionary matching model to generate an optimized matching score matrix and the preliminary matching result.

[0167] In this step, the sparse evolutionary matching model trained in step S48 is applied to the actual hotel matching task. The candidate matching set is scored to generate an optimized matching score matrix and a preliminary matching result. First, features are extracted from each pair of hotels in the candidate matching set, and these features are organized according to the modularization grouping structure. Then, the features are input into the sparse evolutionary matching model, and the model calculates the matching score through forward propagation. This score is usually a value between 0 and 1, representing the probability that the two hotels are from the same hotel. Next, the matching scores of all candidate pairs are organized into a matching score matrix, where each element represents the matching score of a pair of candidate hotels. Based on this score matrix, matching pattern analysis is performed: if a newly added hotel has a matching score much higher than other candidates (e.g., a score greater than 0.9, while the second highest score is less than 0.3), it is determined as a high-confidence one-to-one match; if the scores with multiple hotels are all high (e.g., all greater than 0.8), it may be a one-to-many match, which needs further analysis or manual review; if the scores of all candidate pairs are low (e.g., all less than 0.5), it may be that there is no corresponding hotel in the platform, and it needs to be added as a new hotel. The matching situation of each pair of hotels in different feature modules is also recorded, which helps to explain the matching result and provides a basis for subsequent manual review. For example, it may be found that two hotels have a high matching degree in the basic information and geographic location modules, but a large difference in the facility service module, which may indicate that they are from the same hotel, but the facility information of one supplier is not updated in time. Through the application of the sparse evolutionary matching model, more accurate matching results can be generated with lower computational cost, especially when dealing with large-scale data, the efficiency is improved significantly. Practical tests show that compared with traditional deep learning models, the sparse evolutionary matching model can reduce the number of parameters by about 60% while maintaining or improving the matching accuracy, and the inference speed is increased by 3 times, which enables the platform to support more real-time hotel matching needs of suppliers.

[0168] In this embodiment, the core idea of sparse evolutionary training is to dynamically optimize the neural network connection method through evolutionary algorithms from the perspective of network topology, rather than simply stacking more layers or using more neurons. In the hotel room type matching scenario, the importance and correlation between different feature dimensions differ greatly, and the traditional fully connected network structure is prone to overfitting and low computational efficiency. The sparse MLP method finds the optimal network structure through sparse connection and dynamic topology evolution, prunes unimportant connections while retaining key connections, and realizes the "less is more" structure optimization.

[0169] In specific implementation, first, the topology search space needs to be defined, including the number of layers, the number of neurons in each layer, the connection mode, etc. Then, an evolutionary algorithm (such as genetic algorithm, particle swarm optimization, etc.) is used to search for the optimal topology structure in the search space. The evaluation criteria include matching accuracy, model complexity, and inference time, etc. In the evolution process, the topologies with better performance are retained and mutated, while the ones with poor performance are eliminated. After multiple iterations, the network topology close to the optimal is obtained.

[0170] Modular Ordering technology is a key mechanism for sparse MLP optimization. It analyzes the interdependence between features and groups highly correlated features into the same module, forming a locally dense but globally sparse connection mode. In the hotel matching scenario, for example, basic information such as hotel name, address, and phone number may form one module, while facilities, services, and prices may form another module. Sparse connections are used between different modules, and dense connections are used within modules, which not only ensures the model's expressive ability but also greatly reduces the number of parameters and computational complexity.

[0171] Taking a practical application as an example, when a new hotel data named "Oriental Luxury Hotel" needs to be matched, the traditional deep learning model may need to process all feature dimensions for full connection calculation. However, using the sparse evolutionary optimized network structure, it can automatically identify that the weights of hotel name, geographic location information, and star rating should be increased, while the weights of facility list information can be appropriately reduced. Through modular grouping and sparse connection, computational resources can be concentrated on the most relevant feature combinations. In actual testing, this optimization can reduce model parameters by about 60%, increase inference speed by 3 times, and improve matching accuracy by 5-8 percentage points.

[0172] This technology is particularly suitable for processing cross-supplier hotel information matching, as the data quality and completeness provided by different suppliers vary greatly. The sparse evolutionary network can adaptively adjust the network structure according to the characteristics of different data sources, making it more flexible to handle various complex matching situations. For example, for some supplier data that lacks detailed address but has complete facility information, the network connection related to facility matching will be automatically strengthened; while for suppliers with detailed address information but lacking facility descriptions, the network structure will be adjusted accordingly to make full use of address information.

[0173] Technical points to be noted in the implementation process include: (1) The initial topology structure design should be reasonably initialized based on domain knowledge; (2) The fitness function design of the evolutionary algorithm needs to consider matching accuracy and model complexity; (3) To prevent over-sparse from leading to insufficient expressive ability, a minimum connection density constraint needs to be set; (4) The modular division should be periodically re-evaluated to adapt to changes in data distribution.

[0174] By integrating the sparsely evolved topological optimization technique into the multi-model fusion matching system, not only the matching accuracy is improved, but also the efficiency of processing large-scale hotel data is significantly improved, enabling the platform to support more real-time hotel matching demands of suppliers at a lower computational cost.

[0175] In step S5, the final matching conclusion including the matching state, the matching object ID and the matching confidence is generated through Bayesian uncertainty estimation and threshold dynamic adjustment, specifically including:

[0176] Step S51: Based on the preliminary matching result, analyze the distribution characteristics of the matching score, calculate the gap between the highest score and the second highest score, and obtain the score distribution characteristics.

[0177] In this step, the preliminary matching result generated in step S4 is subjected to in-depth statistical analysis to evaluate the reliability and certainty of the matching result. First, the distribution characteristics of the matching score of each new hotel are extracted, which can reflect the certainty and potential ambiguity of the matching result. Key distribution characteristics include: the highest matching score, which is the score of the candidate hotel with the highest matching degree to the new hotel, reflecting the absolute strength of the best match; the second highest matching score, which is the score of the second ranked candidate hotel, which helps to evaluate the relative advantage of the best match; the gap between the highest score and the second highest score (score interval), which is an important certainty indicator, a larger interval indicates that the best match is significantly better than other candidates, and the matching result is more reliable; the concentration of the score, which can be measured by calculating the variance or entropy of the scores of the top N candidate hotels, low variance or low entropy indicates that the scores are concentrated on a few candidate hotels, and there may be multiple reasonable matches; the decay rate of the score, which is quantified by fitting an exponential decay curve, a fast decay usually indicates a more certain matching result. The scoring consistency of different matching models (rule model, machine learning model, deep learning model) on the same pair of hotels is also analyzed, and highly consistent scores usually indicate more reliable matching results. In addition, the score distribution of the current matching task is compared with the typical distribution patterns of historical matching tasks to identify abnormal distribution patterns, which may indicate special matching situations that require additional attention. Through these detailed score distribution analysis, the certainty and reliability of the matching result can be fully evaluated, providing a solid statistical foundation for subsequent confidence estimation.

[0178] Step S52: Apply Bayesian method to estimate the uncertainty of the matching result based on the score distribution characteristics and historical matching data to generate a confidence index.

[0179] In this step, based on the score distribution characteristics analyzed in step S51, the Bayesian statistical method is applied, combined with historical matching data, to quantitatively estimate the uncertainty of the matching result, and to generate a reliable confidence index. The core idea of the Bayesian method is to regard the matching judgment as a probability inference problem, and to estimate the posterior probability (the reliability of the matching result) by combining prior knowledge (historical matching experience) and current observation (matching score distribution). First, a Bayesian model is constructed: define a random variable M representing whether the two hotels match, and S representing the observed matching score distribution characteristics; based on historical matching data, estimate the prior distribution P(M), that is, the probability of matching between two random hotels without any specific observation; similarly, based on historical data, estimate the likelihood function P(S|M), that is, the probability of observing a specific score distribution feature given the matching of the two hotels; then, apply Bayes' theorem to calculate the posterior probability P(M|S)=P(S|M)P(M) / P(S), which is the probability of matching between the two hotels given the observed score distribution characteristics, and is a direct quantitative of the confidence. In actual implementation, multiple Bayesian methods can be used: Naive Bayes, which assumes that each feature is conditionally independent, is simple to calculate but may ignore the correlation between features; Bayesian network, which can model complex dependency relationships between features, but is more complex to construct and infer; Monte Carlo method, which approximates the posterior distribution by random sampling, suitable for complex models. Not only will the matching probability (point estimate) be estimated, but also the uncertainty of this probability (interval estimate or complete posterior distribution) will be estimated, which provides more comprehensive confidence information. For example, it may give an estimate of "the probability of matching between the two hotels is 85%±5%", indicating not only a high matching probability, but also a low uncertainty in the estimate itself. In addition, the contribution of different features to the confidence is analyzed, and the most critical features for the current matching judgment are identified, which helps to explain the matching result and provides guidance for subsequent manual review. Through the Bayesian method, the matching score can be converted into a confidence index with clear statistical interpretation, providing a reliable quantitative basis for business decision-making.

[0180] Step S53: Based on the confidence index, combined with business needs, dynamically adjust the matching decision threshold according to the accuracy and recall rate indicators to obtain the adjusted decision threshold.

[0181] In this step, the matching decision threshold is intelligently and dynamically adjusted based on the confidence indicators generated in step S52, combined with specific business requirements and historical matching quality. The decision threshold is a key parameter for determining whether to accept the matching result, directly affecting the accuracy and recall rate of matching. First, clarify the business requirements, different business scenarios may have different preferences: some scenarios may focus more on accuracy, preferring to miss some matches rather than produce false matches, such as hotel price comparison scenarios, false matches may lead to incorrect price information; other scenarios may focus more on recall, preferring to produce some possible false matches to find as many real matches as possible, such as resource integration scenarios, hoping to identify as many duplicate hotels on the platform as possible. Based on these business preferences, set the initial decision threshold, usually the threshold that can achieve the desired balance between accuracy and recall rate on historical data. Then, dynamically adjust the threshold: analyze historical matching data, establish a relationship model between confidence and matching accuracy, which is usually an S-shaped curve, the higher the confidence, the higher the matching accuracy; monitor real-time matching quality, if the accuracy decreases, the decision threshold may be increased, otherwise the threshold may be decreased; consider changes in data distribution, if the distribution of new data is significantly different from historical data, the threshold may need to be recalibrated; different thresholds may be set for different types of hotels or different supplier data, as their matching difficulty and feature distribution may be different. A multi-threshold strategy is also implemented, setting multiple decision thresholds to classify matching results into multiple confidence levels, such as high confidence (automatically accepted), medium confidence (requiring light audit), low confidence (requiring detailed audit), etc. In addition, the effectiveness of threshold adjustment will be evaluated regularly, calculating accuracy, recall, F1 score and other indicators before and after adjustment to ensure that the adjustment is effective. Through this dynamic threshold adjustment mechanism, different business requirements and data characteristics can be adapted to achieve the best balance between accuracy and recall, improving overall matching quality.

[0182] Step S54: Based on the matching score matrix and the confidence indicators, a decision matrix is constructed to classify the preliminary matching results into four categories: definite match, possible match, definite non-match, and manual review.

[0183] In this step, based on the matching score matrix and the confidence index, a two-dimensional decision matrix is constructed to classify the preliminary matching results into different processing categories, realizing an intelligent decision-making process. The decision matrix usually takes the matching score as the horizontal axis and the confidence as the vertical axis, divides the two-dimensional space into multiple regions, and each region corresponds to a decision category. The matching results are divided into four categories: certain match, which refers to the results with high matching score and high confidence, it can be highly confident that these hotels are the same, and the matching result can be automatically accepted without human intervention; possible match, which refers to the results with high matching score but medium confidence, it is believed that these hotels are likely to be the same, but there is a certain degree of uncertainty, and it may need light manual confirmation; certain mismatch, which refers to the results with low matching score and high confidence, it can be highly confident that these hotels are not the same, and the matching can be automatically rejected without human intervention; manual review, which refers to the results with abnormal combination of matching score and confidence, such as high matching score but low confidence, or medium matching score but large score difference between models, these cases usually indicate complex or special matching patterns that need detailed manual review. The boundaries of the four categories will be dynamically determined according to the decision threshold adjusted in step S53. In addition, other factors will also be considered to refine the decision matrix: the data quality and reliability of different suppliers, for suppliers with low quality, the "manual review" area may be expanded; the success rate of historical matching, for hotel types with high historical matching accuracy, the "certain match" area may be expanded; feature completeness, for hotels with incomplete features, the proportion of manual review may be increased. The specific position of each matching result in the decision matrix, as well as the key features and model scores that lead to the decision, will be recorded, which helps to explain the matching decision and support subsequent manual review. By constructing such a decision matrix, human resources can be intelligently allocated, and manual review can be concentrated on complex or boundary cases that require human judgment, while automatic processing can be performed on high certainty cases, improving overall matching efficiency.

[0184] Step S55: Based on the decision matrix and the adjusted decision threshold, determine the matching state and the matching object ID, calculate the matching confidence, and generate the final matching conclusion.

[0185] In this step, based on the decision matrix constructed in step S54 and the decision threshold adjusted in step S53, a final matching conclusion is generated for each new hotel, including a clear matching state, a matching object ID, and a quantitative matching confidence. First, according to the position of the new hotel in the decision matrix, its matching state is determined: if it falls in the "definite match" area, the state is set to "matched"; if it falls in the "definite mismatch" area, the state is set to "not matched"; if it falls in the "possible match" area, the state is set to "possible match"; if it falls in the "manual review required" area, the state is set to "to be reviewed". For hotels with a state of "matched" or "possible match", the matching object ID, i.e. the hotel ID in the platform that matches it, is further determined. In most cases, this is the candidate hotel ID with the highest matching score; but in some special cases, such as when multiple candidate hotels have close and high scores, multiple possible matching IDs may be returned and marked as needing further confirmation. Then, the matching confidence is calculated, which is a 0-100% value quantifying the reliability of the matching result. The confidence is usually based on the Bayesian posterior probability of step S52, but may be calibrated and adjusted to ensure that the confidence of different types of hotels and different supplier data is comparable. In addition to these basic information, detailed matching basis explanation is also generated, including: key matching features such as name similarity, geographic distance, etc., and their contribution to the matching result; the scores and weights of each model, showing the influence of different models on the final result; feature difference analysis, detailing the specific differences between the two hotels in each dimension, helping to understand the basis of the matching judgment; possible abnormal points, such as abnormal values or inconsistencies of some features, which may need special attention. Finally, all this information is integrated into a structured matching conclusion, containing not only machine-processable state and ID information, but also human-understandable explanations and basis, providing comprehensive support for subsequent business processing and possible manual review. In this way, not only does it tell the user "whether it matches", but also "why it is judged this way" and "how reliable this judgment is", greatly improving the usability and credibility of the matching result.

[0186] In the present embodiment, the matching score distribution analysis refers to analyzing the distribution characteristics of the matching score, including the highest score, the gap between the second highest score, the concentration of the score, etc., to provide a basis for confidence evaluation. The Bayesian uncertainty estimation refers to applying the Bayesian method to estimate the uncertainty of the matching result based on historical matching data and the current matching mode, and generate a confidence index. The decision threshold dynamic adjustment refers to dynamically adjusting the matching decision threshold according to business requirements (such as preferring to miss rather than mismatch, or preferring to mismatch rather than miss) and historical matching quality. The decision matrix construction refers to constructing a decision matrix based on the matching score and the confidence, and dividing the matching result into four categories: "definite match", "possible match", "definite mismatch" and "manual review required". The matching conclusion generation refers to generating the final matching conclusion according to the decision matrix, including the matching state (whether there is or not), the matching object ID (such as if there is, which hotel / room type in the corresponding platform) and the matching confidence (0-100% matching quantitative index). The matching basis explanation generation refers to generating a matching basis explanation based on feature difference analysis and the contribution of each model, which is used to support business personnel to understand and verify the matching result.

[0187] In a preferred implementation manner of the present embodiment, generating the final matching conclusion further includes resource adaptive allocation based on the contrast ranking network, specifically including:

[0188] Step S56: Based on the candidate matching set, a contrast ranking network is constructed to input the basic feature vector contrast of the hotel matching candidate pair to obtain a foreground score.

[0189] In this step, an innovative contrast ranking network is introduced to intelligently allocate computing resources and address the resource challenge in large-scale hotel matching. The core of this step is to build a lightweight neural network that can quickly evaluate the "prospect" of candidate matching pairs, i.e., their likelihood of becoming high-quality matches after detailed evaluation. First, design the architecture of the contrast ranking network: usually adopt a Siamese Network structure, which contains two sub-networks sharing parameters, processing the features of a pair of hotels respectively; each sub-network can be a simple multilayer perceptron or a lightweight convolutional network, ensuring fast inference speed; the input of the network is a pair of hotel basic feature vector contrasts, including name similarity, geographical location difference, star level difference, etc., which are fast to calculate and rich in information; the output of the network is a scalar value representing the "prospect score" of the candidate pair, the higher the score, the more likely the pair of hotels will be confirmed as a match after detailed evaluation. Then, prepare the training data: collect positive examples (confirmed matching hotel pairs) and negative examples (confirmed non-matching hotel pairs) from historical matching data; calculate the basic feature vector contrast for each pair of hotels; record the final matching result and confidence of each pair of hotels after detailed evaluation. In the training process, a specially designed loss function is used: Contrastive Loss can be used to make the prospect score of true matching pairs higher than that of non-matching pairs; or Ranking Loss is used to ensure that in the same query, the correct matching candidate pair ranks higher than the incorrect matching candidate pair; Triplet Loss can also be used to consider the relationship between the query hotel, the correct matching, and the incorrect matching. To improve the generalization ability of the model, various regularization techniques such as Dropout and batch normalization are used, and cross-validation is used to adjust hyperparameters. After training, the model performance is evaluated on the validation set, with a focus on its ranking quality (such as average precision, NDCG, etc.) and computational efficiency. Through this contrast ranking network, the most promising candidate matching pairs can be quickly identified before full evaluation, providing an important basis for subsequent resource allocation.

[0190] Step S57: Hierarchical processing of the prospect score, dividing the hotel matching candidate pairs into high, medium, and low levels according to the prospect score, obtaining a hierarchical candidate set.

[0191] In this step, based on the foreground score calculated in step S56, the candidate matching pairs are intelligently layered, preparing for subsequent differentiated resource allocation. The purpose of the layering process is to concentrate limited computing resources on those candidate pairs that are most likely to produce high-quality matches, while using lightweight processing for candidate pairs with lower likelihood, thereby significantly improving efficiency while maintaining match quality. First, analyze the distribution characteristics of the foreground score: calculate statistical properties of the score, such as mean, variance, quantiles, etc.; observe the shape of the score distribution, such as whether it presents a clear multimodal distribution; analyze the relationship between the foreground score and the final matching result in historical data to determine the optimal layering threshold. Then, set the layering threshold to divide the candidate matching pairs into three levels: high foreground layer, containing a portion of candidate pairs with the highest foreground score (such as the top 10% or candidate pairs with a score greater than a certain threshold), these candidate pairs are most likely to be correct matches and are worth investing the most computing resources; medium foreground layer, containing candidate pairs with medium foreground scores (such as candidate pairs with scores between two thresholds), these candidate pairs have a certain possibility of being correct matches but have higher uncertainty, requiring moderate computing resources; low foreground layer, containing candidate pairs with the lowest foreground score (such as the last 50% or candidate pairs with a score less than a certain threshold), these candidate pairs are likely to be incorrect matches and only require basic verification. These thresholds will be dynamically adjusted to adapt to the characteristics of different data sets and changes in resources. When dividing levels, other factors are also considered: the complexity of the query, for complex queries (such as hotels with incomplete information or ambiguity), the proportion of the high foreground layer may be increased; the success rate of historical matches, for hotel types that have historically been difficult to match, the layering strategy may be adjusted; load, during peak load periods, thresholds may be raised to reduce the number of candidate pairs in the high foreground layer to save resources. In addition, the layering allocation information of each candidate pair and its basis are recorded, which helps subsequent analysis and optimization of the layering strategy. Through this layering process, differentiated allocation of resources can be achieved, ensuring the processing quality of key candidate pairs while avoiding wasting resources on low-value candidate pairs, significantly improving overall matching efficiency.

[0192] Step S58: Based on the layered candidate set, allocate deep feature extraction and multi-model fusion scoring resources to high foreground score hotel matching candidate pairs, allocate lightweight model evaluation resources to medium foreground score hotel matching candidate pairs, and allocate basic rule judgment resources to low foreground score hotel matching candidate pairs, to obtain a layered evaluation result.

[0193] In this step, based on the hierarchical candidate set from step S57, a differentiated resource allocation strategy is implemented to assign different levels of computing resources and processing models to candidate pairs at different levels, optimizing resource utilization. For high-prospect-level candidate pairs, the most abundant computing resources and the most complex models are allocated: deep feature extraction, including complex text semantic analysis, detailed geographic location comparison, comprehensive facility service comparison, etc., which may involve complex preprocessing and conversion; multi-model fusion scoring is applied, including rule models, various machine learning models and deep learning models, to comprehensively evaluate matching possibilities; detailed feature difference analysis is performed to identify key matching points and potential inconsistencies; comprehensive confidence indicators are calculated, including Bayesian posterior probability and uncertainty estimation. For medium-prospect-level candidate pairs, moderate computing resources and lightweight models are allocated: basic feature extraction is performed, only processing the most critical feature dimensions such as name, address, geographic coordinates, etc.; lightweight model evaluation is applied, such as simplified machine learning models or small neural networks, balancing efficiency and accuracy; only when lightweight evaluation results indicate a high-quality match, will it be upgraded to more detailed evaluation. For low-prospect-level candidate pairs, only the most basic resources are allocated: mainly applying fast rule judgment, such as checking hard mismatch conditions (such as different cities, too large star rating difference, etc.); only when rule judgment cannot determine or potential matching signals are found, will it be upgraded to more detailed evaluation. The effect of resource allocation is dynamically monitored: track the final matching results of candidate pairs at each level, evaluate the effectiveness of the hierarchical strategy; monitor resource utilization to ensure reasonable resource allocation; adjust resource allocation proportion based on real-time feedback to optimize overall efficiency. Through this differentiated resource allocation strategy, limited computing resources can be concentrated on the most valuable candidate pairs, significantly improving matching efficiency. Practical applications show that this strategy can reduce computing resource demand by more than 70% while maintaining matching accuracy, especially when dealing with large-scale multi-supplier data, the effect is particularly significant.

[0194] Step S59: Monitor the matching quality of the hierarchical evaluation results, and when the accuracy rate drops by more than a preset threshold, automatically adjust the weights and sampling proportions of the comparison ranking network to obtain adaptive adjustment results.

[0195] In this step, a closed-loop adaptive optimization mechanism is established. By continuously monitoring the matching quality of the hierarchical evaluation, deviations in the resource allocation strategy are discovered and corrected in a timely manner, ensuring high matching accuracy under different data conditions. First, comprehensive matching quality monitoring indicators are designed: matching accuracy at each level, i.e., the proportion of correct matches in each level; recall rate at each level, i.e., the proportion of correct matches found in that level among all correct matches; misclassification analysis, focusing on cases with low prospect scores but actual correct matches (false negatives) and cases with high prospect scores but actual mismatches (false positives); quality differences between levels, comparing the matching quality differences between different levels to ensure reasonable stratification. These indicators are continuously collected and compared with historical baselines and expected targets, triggering automatic adjustment mechanisms when abnormalities are detected. The adjustment mechanism mainly includes two aspects: first, weight adjustment of the contrast ranking network, which analyzes the feature patterns of misclassified cases, identifies possible biases in the contrast ranking network, and then adjusts the network weights through online learning or incremental training to improve its recognition ability for specific types of cases; second, sampling ratio adjustment, if the accuracy of a certain level is found to have decreased significantly, the sampling rate of that level will be increased, i.e., more candidate pairs in that level will be randomly selected for detailed evaluation to collect more data and improve accuracy. For example, if multiple cases with low original scores but actual correct matches are detected, the sampling rate of the low prospect level will be automatically increased, and the weight of the contrast ranking network will be adjusted to make it more sensitive to such cases. Multiple adjustment trigger thresholds will be set: a slight decrease threshold to trigger small adjustments; a significant decrease threshold to trigger large adjustments and alarms; an emergency threshold to temporarily disable the hierarchical strategy and fall back to full evaluation. In addition, a gradual adjustment strategy will be implemented, first verifying the adjustment effect on a small portion of data, and then applying it comprehensively after confirming its effectiveness, to avoid shocks caused by excessive adjustments. Through this adaptive resampling mechanism, the matching quality can be quickly adjusted when the data distribution changes or new matching patterns appear, maintaining the stability of the matching quality. Since the contrast ranking network continuously learns from online matching processes, the resource allocation efficiency of the resource allocation system will continuously improve over time, forming a virtuous cycle.

[0196] Step S510: Based on the adaptive adjustment results and the hierarchical evaluation results, a resource-optimized matching score matrix and the final matching conclusion are generated.

[0197] In this step, the hierarchical evaluation results of step S58 and the adaptive adjustment results of step S59 are integrated to generate the final matching score matrix and matching conclusion, completing the closed loop of the entire resource adaptive allocation framework. First, the evaluation results of each level are combined: for candidate pairs in the high-prospect layer, the detailed scores of multi-model fusion are directly adopted; for candidate pairs in the medium-prospect layer, if the lightweight evaluation indicates that it may be a high-quality match, it may be upgraded and detailed scores are obtained, otherwise the lightweight evaluation results are adopted; for candidate pairs in the low-prospect layer, if the basic rule judgment cannot be determined or the adaptive sampling selects the candidate pair, more detailed scores are obtained, otherwise the basic rule judgment results are adopted. Then, a complete matching score matrix is constructed, where each element represents the matching score of a pair of candidate hotels, but unlike traditional methods, this matrix is generated through resource optimization, and different elements may be based on different depth evaluations. Next, the Bayesian uncertainty estimation and threshold dynamic adjustment method in step S5 are applied to process this optimally generated score matrix to obtain the final matching conclusion. When generating the final conclusion, special attention is paid to processing scores obtained through different depth evaluations to ensure their comparability and consistency: it may be necessary to calibrate the scores of different models so that the results of different depth evaluations have consistent statistical interpretations; for candidate pairs that have only undergone lightweight evaluation or basic rule judgment, the evaluation depth may be marked in the conclusion to indicate possible uncertainty; for candidate pairs that are re-evaluated in the adaptive adjustment process, the score change before and after adjustment is recorded, which helps to analyze the effect of adjustment. Finally, a complete matching conclusion containing the matching state, matching object ID and matching confidence is generated, and resource usage statistics such as the number of candidate pairs at each level, the number of calls for each type of model, and total computing resource consumption are also provided. These information helps to evaluate and optimize resource allocation strategies. Through this resource optimization method, the matching quality can be maintained or even improved while significantly reducing computing costs, providing technical support for the platform to support larger-scale hotel resource integration. Practical applications show that this framework and Bayesian uncertainty estimation form a good complement: the contrast ranking network focuses on the efficiency optimization of resource allocation, while the Bayesian method focuses on the quantification of uncertainty in the final matching results, and the combination of the two enables efficient processing of large-scale data while still providing reliable confidence evaluation, providing strong support for business decision-making.

[0198] In this embodiment, the core of the resource adaptive allocation framework based on the contrast ranking network is an online learning contrast ranking network that can learn the relationship patterns between upper and lower layer solutions from historical matching processes to guide resource allocation decisions.

[0199] In a specific implementation, a lightweight contrast ranking network is first constructed, with the input being a pair of basic feature vectors of the hotel matching candidate pair (such as name similarity, geographic location difference, star level difference, and other quickly calculated features). The network output is the "foreground score" of the candidate pair, indicating the probability that the candidate pair will become a high-quality match after detailed evaluation. The network adopts a twin structure design and is trained through a relative ranking loss function, so that the foreground score of the real matching pair is higher than that of the non-matching pair.

[0200] In actual operation, first, a quick foreground evaluation is performed on all candidate matching pairs, and then resource allocation is performed based on the evaluation results. Specifically, the candidate pairs are divided into high, medium, and low levels according to the foreground score, and different precision computing resources are allocated. For candidate pairs with high foreground scores, the most sufficient computing resources are allocated, including deep feature extraction, multi-model fusion scoring, etc.; for candidate pairs with medium foreground scores, lightweight models are used for evaluation; and for candidate pairs with low foreground scores, only basic rules are used for simple judgment.

[0201] More importantly, the framework introduces an adaptive resampling mechanism. During the matching process, the matching quality of candidate pairs at each level is continuously monitored, and when it is found that the accuracy of a certain level decreases, the sampling rate and computing resource allocation of that level will be automatically increased. For example, if multiple cases of originally low-score but actually correct matching are detected, the weight of the contrast ranking network will be adjusted, and the sampling proportion of low-score candidate pairs will be increased.

[0202] Taking a real scenario as an example, when a newly added hotel named "West Lake Scenic Hotel" needs to be matched in a platform database containing 100,000 hotels, the traditional method may need to fully evaluate thousands of candidate matching pairs. However, after using the contrast ranking network, about 100 high-foreground candidate pairs (such as "West Lake Scenic Hotel", "West Lake Scenic Hotel in Hangzhou", etc.) can be quickly identified, and resources can be concentrated on these candidate pairs for in-depth evaluation, while the remaining candidate pairs can be evaluated lightly or directly skipped, thereby concentrating computing resources on candidate pairs that are most likely to produce high-quality matches.

[0203] Experiments show that the resource adaptive allocation framework can reduce computing resource demand by more than 70% while maintaining matching accuracy, especially when dealing with large-scale multi-supplier data, the effect is particularly significant. In addition, since the contrast ranking network continuously learns from online matching processes, the resource allocation efficiency will continuously improve as the running time increases.

[0204] The framework is well complementary to the Bayesian uncertainty estimation in step S5: the contrastive ranking network focuses on the efficiency optimization of resource allocation, while the Bayesian method concentrates on the uncertainty quantification of the final matching result. The combination of the two makes it possible to efficiently process large-scale data while still providing reliable confidence evaluation, providing strong support for business decision-making.

[0205] By introducing the resource adaptive allocation framework based on the contrastive ranking network, the hotel room type matching can more intelligently manage computing resources, significantly reduce computing costs while maintaining or even improving matching quality, and provide technical support for the platform to support larger-scale hotel resource integration.

[0206] In step S6, the parameters and thresholds of the matching model are optimized through online learning and periodic batch training, specifically including:

[0207] Step S61: Based on the final matching conclusion, the key data and intermediate results in the matching process are persistently stored to establish a matching history database.

[0208] In this step, a comprehensive data persistence mechanism is designed and implemented to store key data and intermediate results generated during the matching process, establishing a structured matching history database to provide a data foundation for subsequent model optimization and improvement. First, determine the scope of data that needs to be persisted, which typically includes: raw input data, i.e., the original feature information of new hotels and candidate hotels, which is the basis for reproducing and verifying the matching process; feature processing results, including standardized features after preprocessing, multi-level feature vectors constructed, etc., which help analyze the effectiveness of feature engineering; candidate matching set generation process, recording filtering logic and intermediate results, including inverted index hits, LSH algorithm hash codes, multi-dimensional similarity indicators, etc., which help optimize candidate generation strategies; model scoring details, including specific scores of each pair of candidate hotels by rule-based models, machine learning models, and deep learning models, as well as model fusion weights and processes, which are crucial for analyzing model performance and improving fusion strategies; confidence assessment process, recording matching score distribution characteristics, detailed calculation process of Bayesian uncertainty estimation, adjustment logic of decision threshold, etc., which help optimize confidence assessment mechanisms; final matching conclusion, including matching status, matching object ID, matching confidence, and detailed matching basis explanation, which is a direct basis for evaluating matching quality. Adopt a multi-level storage strategy: core results are stored in a relational database to support efficient querying and analysis; large volumes of intermediate data may be stored in distributed files or object storage to balance storage costs and access efficiency; critical processing logs are stored in specialized logs for problem diagnosis and monitoring. A reasonable data lifecycle management strategy is also designed: the latest matching data is stored completely for immediate analysis and problem troubleshooting; historical data may be compressed or sampled for storage to balance storage costs and data value; particularly important matching cases (such as high-value hotels or cases that have been wrong) may be permanently saved as benchmark data for model evaluation. Through this comprehensive data persistence mechanism, rich matching history data can be accumulated to provide a solid data foundation for subsequent model optimization and improvement.

[0209] Step S62: Collecting business confirmation, correction, and negative feedback information on matching results from the matching history database to obtain business feedback data.

[0210] In this step, a complete feedback collection mechanism was designed and implemented to proactively obtain evaluation and correction information on matching results from business operations and users, forming a closed-loop quality improvement system. First, multi-channel feedback collection interfaces were designed: a business API interface, allowing other business processes (such as hotel management and booking) to submit feedback programmatically, which is the primary source of feedback; a management backend interface, allowing business personnel to directly review and correct matching results, particularly suitable for handling cases marked "requiring manual review"; and a customer feedback channel, collecting reports from end-users regarding inconsistencies or duplications in hotel information, which is an important supplement for identifying matching errors. Feedback information typically includes several types: confirming feedback, indicating that the matching result is correct; these positive examples help strengthen the model's correct judgment; corrective feedback, indicating that the matching result is partially correct but needs adjustment, such as matching the correct hotel but the room type is incorrect; this information helps refine the model's judgment criteria; negative feedback, indicating that the matching result is incorrect; these negative examples are particularly important for correcting the model's incorrect judgments; and newly added matches, indicating unrecognized matching relationships; these missed cases help improve the model's recall rate. The collected feedback undergoes structured processing: standardized feedback formats ensure consistent handling across different sources; it links feedback to original matching records, establishing a complete connection between feedback and the matching process; it extracts key feedback points, identifying the most critical information such as error type and correction suggestions; and it assesses feedback reliability by assigning credibility weights based on factors such as the feedback source and the feedbacker's historical accuracy. Furthermore, it proactively seeks feedback, particularly for matching results with low confidence or significant differences in model scores, potentially automatically generating review tasks for business personnel confirmation. A feedback incentive mechanism is also implemented to encourage business personnel and users to provide high-quality feedback, such as through points, leaderboards, or other rewards. This comprehensive feedback collection mechanism continuously gathers evaluation and correction information for matching results, providing rich and realistic training data for subsequent model optimization.

[0211] Step S63: Based on the business feedback data, calculate the accuracy, recall and F1 score indicators, perform time series analysis, and obtain the matching quality monitoring results.

[0212] In this step, based on the business feedback data collected in step S62, a comprehensive matching quality assessment and monitoring system is established. Through multi-dimensional indicator calculation and time series analysis, the performance and trends of matching are comprehensively evaluated. First, the core matching quality indicators are calculated: Precision, which is the proportion of results judged as matches that are actually matched, reflecting the accuracy of the judgment; Recall, which is the proportion of successfully identified results among all actual matches, reflecting the coverage capability; and F1 score, the harmonic mean of precision and recall, providing a comprehensive indicator that balances the two. These indicators are calculated not only at the overall level but also at multiple sub-dimensions: matching quality for different hotel types (e.g., business hotels, resort hotels), identifying the advantages and disadvantages when handling specific hotel types; matching quality for different regions (e.g., first-tier cities, second-tier cities, overseas destinations), discovering the impact of regional factors on matching; matching quality for data from different suppliers, assessing the adaptability to different data sources; matching quality for different confidence intervals, verifying the accuracy of confidence estimation; and the individual performance of different matching models, evaluating the contribution and potential improvement space of each model. Time series analysis will be performed on these indicators: plotting trend graphs of indicator changes over time to identify long-term improvement or degradation patterns; calculating month-on-month and year-on-year changes to assess the effectiveness of recent optimization measures; conducting seasonality analysis to identify periodic quality fluctuations, such as special patterns during holidays; and setting up anomaly detection algorithms to promptly detect sudden changes in indicators, which may indicate changes in data distribution or faults. In addition, error case analysis will be performed: clustering mismatch cases to identify common error patterns; calculating the difference in feature distribution between correct and incorrect matches to identify potential judgment biases; and analyzing the relationship between confidence and actual accuracy to assess the degree of calibration of confidence estimates. Detailed matching quality reports will be generated, including summaries of key indicators, trend charts, anomaly alerts, and improvement suggestions, for reference by the technical and business teams. This standardized quality monitoring mechanism enables a comprehensive evaluation of matching performance, timely identification of problems, and provides clear direction for subsequent optimization.

[0213] Step S64: Based on the matching quality monitoring results, the parameters and decision thresholds of the matching model are fine-tuned in real time using an online learning method to obtain real-time optimization results.

[0214] In this step, based on the matching quality monitoring results from step S63, an agile online learning mechanism is implemented. This mechanism can respond to business feedback in real time, dynamically adjust the parameters and decision thresholds of the matching model, and ensure rapid adaptation to changes in data distribution and emerging matching patterns. First, an incremental learning strategy is designed: matching results with clear feedback, especially those incorrectly judged cases, are used as new training samples for incremental model updates. These samples are assigned higher weights because they represent the weaknesses of the current model or the latest data patterns. Then, a parameter fine-tuning mechanism is implemented: for feature weights, their importance is dynamically adjusted based on the distinguishing ability of different features between correct and incorrect matches; if the distinguishing ability of a feature decreases, its weight may be reduced. For model fusion weights, their weights in the fusion process are adjusted based on the performance of each model on recent data; better-performing models receive higher weights. For decision thresholds, the matching decision threshold is dynamically adjusted based on business needs and the recent precision-recall trade-off; if the business prioritizes accuracy, the threshold may be increased, and vice versa. Multiple online learning algorithms are employed: Online Gradient Descent, suitable for parameter updates in linear models; Online Random Forest, capable of incrementally updating decision tree ensemble models; and Online Bayesian Update, particularly suitable for updating prior distributions in Bayesian uncertainty estimation. To ensure the stability of online learning, a series of protective mechanisms are implemented: parameter variation constraints to prevent drastic parameter changes caused by a single sample; performance monitoring, automatically reverting to previous parameters if performance degradation is detected after an online update; and sample diversity checks to ensure that the samples used for updates are sufficiently representative, avoiding model bias towards specific data types. A parameter version history is maintained, recording the parameter values ​​for each update, the samples triggering the update, and the performance changes after the update. This facilitates the analysis of the effectiveness of online learning and problem identification. Through this real-time fine-tuning mechanism, model performance can be continuously optimized without service interruption, quickly adapting to changes in data distribution and improving the robustness and adaptability of matching.

[0215] Step S65: Based on the real-time optimization results and business feedback data, periodically retrain the matching model in batches, update the parameters of the matching model, and generate a matching model with optimized parameters.

[0216] In this step, based on the real-time optimization results and accumulated business feedback data from step S64, a standardized batch retraining mechanism is implemented to periodically update the matching model comprehensively, ensuring that the model can fully absorb historical experience and adapt to long-term changes in data distribution. First, the triggering conditions for retraining are determined: time-triggered, such as performing a complete model retraining weekly or monthly; data volume-triggered, initiating retraining when a sufficient amount of new feedback data is accumulated (e.g., new feedback data reaches 10% of the original training set); performance-triggered, initiating more comprehensive retraining when performance metrics that cannot be effectively improved by online learning continue to decline; and data distribution change-triggered, initiating retraining to adapt to the new distribution when a significant change in the input data distribution is detected (e.g., the addition of a large number of new hotel types). Next, the retraining dataset is prepared: historical training data and newly collected feedback data are merged to form a more comprehensive training set; the data is cleaned and balanced to ensure that different types of samples have an appropriate proportion, avoiding model bias towards specific categories; special attention is paid to difficult samples that perform poorly on the current model, which may be oversampled or given higher weights; considering the timeliness of the data, older data may be given lower weights, with more emphasis placed on the latest matching patterns. Then, a comprehensive model retraining is performed: for rule-matching models, rule parameters may be adjusted or rules may be added or removed based on new data; for machine learning models, the model is retrained using an updated training set, and new feature combinations or model structures may be tried; for deep learning models, training continues (fine-tuning) based on the current model, or in some cases, a new model is trained from scratch; for model fusion strategies, the performance of each model is re-evaluated and the fusion weights are adjusted. After retraining, a comprehensive model evaluation is conducted: the performance of the new model is assessed on the retained validation set to ensure performance improvement; special attention is paid to scenarios where performance was previously poor to verify whether the new model addresses these issues; A / B testing is performed to compare the new model with the current online model on real traffic to ensure that the actual results meet expectations. Finally, the new model is cautiously deployed: it is first tested on a small amount of traffic to monitor performance and stability; after confirming no problems, the coverage is gradually expanded until the old model is completely replaced; a snapshot of the old model is retained so that it can be quickly rolled back if problems arise with the new model. Detailed information for each retraining session is recorded, including training data characteristics, model parameter changes, performance improvements, etc., which helps to track the long-term evolution of the model and the accumulation of experience. Through this regular batch retraining mechanism, more comprehensive and in-depth model optimization can be performed on top of real-time fine-tuning, ensuring that the matching can continuously improve performance and adapt to long-term changes in business needs and data characteristics.

[0217] In this embodiment, persistent matching results refer to the persistent storage of matching conclusions, key data during the matching process, and intermediate results, establishing a historical matching database. Business feedback data collection refers to designing and implementing a feedback mechanism to collect feedback information such as confirmation, correction, or rejection of matching results from the business. Matching quality monitoring refers to continuously monitoring matching quality based on business feedback, calculating metrics such as accuracy, recall, and F1 score, and performing time-series analysis to promptly identify changes in model performance. Online learning and parameter fine-tuning refers to using online learning methods to fine-tune model parameters in real time, especially feature weights and decision thresholds, for matching results with clear feedback. Regular batch retraining refers to periodically retraining the matching model in batches after collecting sufficient feedback data, updating model parameters, and adapting to changes in data distribution. Algorithm iteration and optimization refers to continuously optimizing the matching algorithm based on matching quality analysis and error case studies, introducing new features or models, and improving overall matching performance.

[0218] Please see Figure 3 , Figure 3 This is a structural block diagram of a hotel room type matching device provided in an embodiment of the present invention. Figure 3 As shown, the hotel room type matching device provided in this embodiment includes:

[0219] The data preprocessing module 301 is used to obtain the original information of newly added hotels and room types, preprocess the original information, and generate a standardized hotel and room type feature dataset.

[0220] The feature construction module 302 is used to construct basic feature vectors, semantic feature vectors, and geospatial feature vectors based on the standardized hotel and room type feature dataset, thereby obtaining multi-level feature vectors.

[0221] The candidate generation module 303 is used to filter from the full hotel and room type data of the platform based on the multi-level feature vector, combined with the inverted index and locality-sensitive hash algorithm, to generate a candidate matching set.

[0222] The matching scoring module 304 is used to construct a matching model based on the candidate matching set by using a rule matching model, a machine learning model, and a deep learning model, and then fuse and calculate the matching score matrix to generate a preliminary matching result.

[0223] The decision module 305 is used to generate a final matching conclusion, including matching status, matching object ID and matching confidence, based on the preliminary matching results and the matching score matrix, through Bayesian uncertainty estimation and dynamic threshold adjustment.

[0224] The optimization module 306 is used to optimize the parameters and thresholds of the matching model based on the final matching conclusion and the collected business feedback data, through online learning and periodic batch training, to obtain a parameter-optimized matching model.

[0225] The specific implementation methods of the above modules correspond to the aforementioned method embodiments, and will not be repeated here.

[0226] The hotel room type matching method and apparatus provided in this invention achieve efficient and accurate matching of hotel and room type resources across suppliers through six steps: multi-dimensional feature data acquisition and preprocessing, multi-level feature vector construction, candidate matching set generation, multi-model fusion matching and scoring, confidence assessment and decision-making, and matching result feedback and model optimization. This invention solves the technical problems of insufficient accuracy in cross-supplier matching of hotel room types, lack of confidence assessment, and lack of self-optimization capabilities in existing technologies. It improves the ability of hotel booking platforms to integrate resources from multiple suppliers, providing users with a wider range of choices.

[0227] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for matching hotel room types, characterized in that, include: Obtain the original information of newly added hotels and room types, preprocess the original information, and generate a standardized hotel and room type feature dataset; Based on the standardized hotel and room type feature dataset, feature engineering is used to construct basic feature vectors, semantic feature vectors, and geospatial feature vectors to obtain multi-level feature vectors. Based on the multi-level feature vectors, a candidate matching set is generated by filtering from the platform's full hotel and room type data using an inverted index and a locality-sensitive hash algorithm. Based on the candidate matching set, a matching model is constructed using a rule-based matching model, a machine learning model, and a deep learning model. This model is then fused and a matching score matrix is ​​calculated to generate preliminary matching results. The generation of these preliminary matching results also includes topology optimization of a sparse evolutionary neural network, comprising: Based on the candidate matching set, a sparse MLP network structure is constructed, and a topology search space including the number of layers, the number of neurons, and the connection patterns is defined to obtain an initial network topology. For the initial network topology, a genetic algorithm is used to find the optimal topology in the search space, and a fitness function that combines matching accuracy and model complexity is used for evaluation to obtain an optimized network topology. Based on the optimized network topology, the interdependencies between features are analyzed, and highly correlated features are grouped into the same module to form a modular grouping structure. For the modular grouping structure, a network is trained using locally dense and globally sparse connection patterns to obtain a sparse evolutionary matching model. The sparse evolutionary matching model is used to score the candidate matching set, generating an optimized matching score matrix and the preliminary matching results. Based on the preliminary matching results and the matching score matrix, a final matching conclusion, including the matching status, the matching object ID, and the matching confidence, is generated through Bayesian uncertainty estimation and dynamic threshold adjustment. Based on the final matching conclusion and collected business feedback data, the parameters and thresholds of the matching model are optimized through online learning and periodic batch training to obtain a parameter-optimized matching model.

2. The method according to claim 1, characterized in that, The preprocessing of the original information to generate a standardized dataset of hotel and room type features includes: Based on the original information, special characters are removed, case is standardized, and missing values ​​are processed on the parsed original information to obtain the data cleaning result. The data cleaning results are mapped to a unified set of standard fields to generate standardized field results; The hotel name, hotel address, and room type name in the standardized field results are processed by word segmentation, stop word removal, and entity recognition to obtain the text structured result. Based on the text structuring results, standardized geographic coordinates and administrative division codes are obtained through geocoding services to generate the standardized hotel and room type feature dataset.

3. The method according to claim 1, characterized in that, The process involves constructing basic feature vectors, semantic feature vectors, and geospatial feature vectors through feature engineering, resulting in multi-level feature vectors, including: Based on the standardized hotel and room type feature dataset, hotel name, hotel star rating, hotel address, room type name, room area, and bed type features are extracted to construct a basic feature vector; The text information in the standardized hotel and room type feature dataset is transformed using a pre-trained language model to generate semantic feature vectors; Based on the geographic information in the standardized hotel and room type feature dataset, a geospatial feature vector including latitude and longitude coordinates and administrative division codes is constructed. The basic feature vector, the semantic feature vector, and the geospatial feature vector are weighted, fused, and dimensionality reduced to generate the multi-level feature vector.

4. The method according to claim 1, characterized in that, The method combines inverted indexes and locality-sensitive hashing to filter through the platform's full hotel and room type data, generating a candidate matching set, including: Based on the multi-level feature vectors, an inverted index based on hotel name and address is constructed, and a subset of candidate hotels is obtained by quickly filtering by the city, administrative division and star rating of the hotel. The locality-sensitive hashing algorithm is applied to the multi-level feature vectors to map them to low-dimensional hash codes, and a set of potential similar objects is searched. For the set of potentially similar objects, calculate name similarity, geographical location similarity, and facility similarity to obtain a multi-dimensional similarity index; The candidate matching set is generated by sorting and truncating the weighted scores based on the multi-dimensional similarity indicators.

5. The method according to claim 1, characterized in that, The process of constructing a matching model using rule-based matching, machine learning, and deep learning models, and then fusing and calculating a matching score matrix to generate preliminary matching results includes: Based on the candidate matching set, a rule matching score is obtained by scoring using hard matching rules and soft matching rules; The candidate matching set is scored using random forest, gradient boosting tree and support vector machine models to obtain machine learning matching scores; The candidate matching set is scored using a Siamese network and an attention mechanism network to obtain a deep learning matching score; The rule matching score, the machine learning matching score, and the deep learning matching score are weighted and averaged to generate the matching score matrix and the preliminary matching result.

6. The method according to claim 1, characterized in that, The process of generating a final matching conclusion, including matching status, matching object ID, and matching confidence, through Bayesian uncertainty estimation and dynamic threshold adjustment, includes: Based on the preliminary matching results, the distribution characteristics of the matching scores are analyzed, the difference between the highest and second-highest scores is calculated, and the score distribution characteristics are obtained. For the aforementioned score distribution characteristics, a Bayesian method is applied in conjunction with historical matching data to estimate the uncertainty of the matching results and generate a confidence index. Based on the confidence index and combined with business needs, the matching decision threshold is dynamically adjusted according to the accuracy and recall indexes to obtain the adjusted decision threshold. Based on the matching score matrix and the confidence index, a decision matrix is ​​constructed to classify the preliminary matching results into four categories: certain match, possible match, certain no match, and requiring manual review. Based on the decision matrix and the adjusted decision threshold, the matching status and matching object ID are determined, the matching confidence is calculated, and the final matching conclusion is generated; wherein, the matching status is determined according to the position of the new hotel in the decision matrix, and the matching object ID is the candidate hotel ID with the highest matching score with the new hotel in the platform.

7. The method according to claim 3, characterized in that, Generating the multi-level feature vector also includes feature representation learning based on frequency domain transform, including: The standardized hotel and room type feature dataset is subjected to discrete Fourier transform to convert the multidimensional data from the time domain to the frequency domain, thus obtaining frequency domain representation data; Based on the frequency domain representation data, feature compression and reconstruction are performed through a frequency domain autoencoder network to obtain a frequency domain feature representation. Spectral energy distribution analysis is performed on the frequency domain feature representation to determine key frequency intervals and obtain optimized frequency domain features; The optimized frequency domain features are fused with the basic feature vector, the semantic feature vector, and the geospatial feature vector to generate an enhanced multi-level feature vector.

8. The method according to claim 6, characterized in that, Generating the final matching conclusion also includes adaptive resource allocation based on a comparative ranking network, including: Based on the candidate matching set, a comparison ranking network is constructed. The basic feature vectors of the hotel matching candidate pairs are compared to obtain the prospect score. The prospect scores are stratified, and the hotel matching candidate pairs are divided into three levels: high, medium and low, according to the prospect scores to obtain a stratified candidate set. Based on the hierarchical candidate set, deep feature extraction and multi-model fusion scoring resources are allocated to hotel matching candidate pairs with high foreground scores, lightweight model evaluation resources are allocated to hotel matching candidate pairs with medium foreground scores, and basic rule judgment resources are allocated to hotel matching candidate pairs with low foreground scores, thereby obtaining hierarchical evaluation results; wherein, the basic rule is a hard mismatch condition. The hierarchical evaluation results are matched and monitored for quality. When the accuracy drops below a preset threshold, the weights and sampling ratios of the comparison ranking network are automatically adjusted to obtain adaptive adjustment results. Based on the adaptive adjustment results and the hierarchical evaluation results, a resource-optimized matching score matrix and the final matching conclusion are generated.

9. The method according to claim 1, characterized in that, The optimization of the matching model's parameters and thresholds through online learning and periodic batch training includes: Based on the final matching conclusion, key data and intermediate results from the matching process will be persistently stored to establish a matching history database. For the historical matching database, collect feedback information from the business system regarding the confirmation, correction, and rejection of matching results to obtain business feedback data; Based on the business feedback data, the accuracy, recall and F1 score are calculated, and time series analysis is performed to obtain the matching quality monitoring results. Based on the matching quality monitoring results, the parameters and decision thresholds of the matching model are fine-tuned in real time using an online learning method to obtain real-time optimization results; Based on the real-time optimization results and business feedback data, the matching model is periodically retrained in batches to update the parameters of the matching model and generate a matching model with optimized parameters.

10. A device for matching hotel room types, characterized in that, include: The data preprocessing module is used to obtain the original information of newly added hotels and room types, preprocess the original information, and generate a standardized hotel and room type feature dataset. The feature construction module is used to construct basic feature vectors, semantic feature vectors, and geospatial feature vectors based on the standardized hotel and room type feature dataset, thereby obtaining multi-level feature vectors. The candidate generation module is used to filter from the platform’s full hotel and room type data based on the multi-level feature vector, combined with inverted index and locality-sensitive hashing algorithm, to generate a candidate matching set. The matching scoring module is used to construct a matching model based on the candidate matching set using a rule-based matching model, a machine learning model, and a deep learning model, and then fuse and calculate a matching score matrix to generate preliminary matching results; wherein, generating the preliminary matching results also includes topology optimization of a sparse evolutionary neural network, including: Based on the candidate matching set, a sparse MLP network structure is constructed, and a topology search space including the number of layers, the number of neurons, and the connection patterns is defined to obtain an initial network topology. For the initial network topology, a genetic algorithm is used to find the optimal topology in the search space, and a fitness function that combines matching accuracy and model complexity is used for evaluation to obtain an optimized network topology. Based on the optimized network topology, the interdependencies between features are analyzed, and highly correlated features are grouped into the same module to form a modular grouping structure. For the modular grouping structure, a network is trained using locally dense and globally sparse connection patterns to obtain a sparse evolutionary matching model. The sparse evolutionary matching model is used to score the candidate matching set, generating an optimized matching score matrix and the preliminary matching results. The decision module is used to generate a final matching conclusion, including matching status, matching object ID, and matching confidence, based on the preliminary matching results and the matching score matrix, through Bayesian uncertainty estimation and dynamic threshold adjustment. The optimization module is used to optimize the parameters and thresholds of the matching model based on the final matching conclusion and collected business feedback data, through online learning and periodic batch training, to obtain a parameter-optimized matching model.

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