Hotel room type matching method and device

Abstract

The invention discloses a hotel room type matching method and device, and the method comprises the steps: obtaining the original information of a newly-added hotel and room type, carrying out the preprocessing of 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 in combination with an inverted index and a locality sensitive hashing algorithm; a matching score matrix is calculated through fusion of the rule matching model, the machine learning model and the deep learning model; generating a final matching conclusion through Bayesian uncertainty estimation and threshold dynamic adjustment; and optimizing the matching model based on the matching conclusion and the business feedback data. Through multi-dimensional feature extraction, multi-model fusion matching and confidence evaluation, the problem of accuracy of hotel room type cross-supplier matching is solved, and the matching efficiency and robustness are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a method and device for hotel room type matching, which is applicable to the integration and matching of hotel and room type resources across suppliers in the tourism accommodation industry. Background Art

[0002] As intermediaries connecting users with hotel resources, hotel booking platforms typically need to integrate resources from multiple hotel suppliers to offer a wide selection. In the tourism accommodation industry, hotels and room types are typically non-standardized resources, unlike standardized commodities with uniform product codes or identification labels.

[0003] Currently, common hotel matching technologies in the industry rely primarily on simple name and location matching. For example, some systems employ string similarity algorithms based on hotel names, or use latitude and longitude information to search for potential identical hotels within a specific radius. Other platforms manually maintain hotel mapping tables to address cross-provider hotel identification.

[0004] Advanced hotel matching technologies typically use a multi-feature fusion approach, integrating information such as hotel name, address, star rating, and amenities as feature vectors. The distance between these feature vectors is then calculated to determine whether a hotel is the same. While this technology performs well when processing structured data, it often struggles with heterogeneous data formats and varying descriptions from different vendors, resulting in suboptimal matching accuracy. This is particularly true when dealing with hotels of the same brand or chain in the same city, or hotels with similar names but different identities.

[0005] The main problems with existing technologies include: first, the lack of multi-dimensional feature extraction and dynamic weight adjustment mechanism for hotels and room types, resulting in insufficient matching accuracy in complex scenarios; second, the lack of a unified confidence assessment system, making it impossible to quantify the reliability of matching results; in addition, most systems fail to effectively utilize historical matching data for model optimization, lack self-learning and evolution capabilities, and require frequent manual intervention to correct incorrect matches. Summary of the Invention

[0006] The purpose of the present invention is to provide a method and device for hotel room type matching, aiming to solve the technical problems in the prior art of insufficient accuracy in hotel room type cross-supplier matching, lack of confidence assessment and self-optimization capabilities.

[0007] To achieve the above object, the present invention provides a method for hotel room type matching, comprising: Obtaining original information of newly added hotels and room types, preprocessing the original information, and generating a standardized hotel and room type feature dataset; Based on the standardized hotel and room type feature dataset, constructing basic feature vectors, semantic feature vectors, and geospatial feature vectors through feature engineering to obtain a multi-level feature vector; Based on the multi-level feature vector, the inverted index and locality sensitive hashing algorithm are combined to filter the hotel and room type data from the platform to generate a candidate matching set; 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 ​​calculated and integrated to generate a preliminary matching result; Based on the preliminary matching results and the matching score matrix, generating a final matching conclusion including matching status, matching object ID and matching confidence through Bayesian uncertainty estimation and threshold dynamic 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 regular batch training to obtain a matching model with optimized parameters.

[0008] Preferably, the pre-processing of the original information to generate a standardized hotel and room type feature dataset includes: Based on the original information, special characters are removed, uppercase and lowercase characters are unified, and missing values ​​are processed on the parsed original information to obtain a data cleaning result; Mapping the data cleaning results to a unified standard field set to generate field standardization results; Performing word segmentation, stop word removal, and entity recognition on the hotel name, hotel address, and room type name in the field standardization results to obtain a text structured result; Based on the text structuring result, standardized geographic coordinates and administrative division codes are obtained through a geocoding service to generate the standardized hotel and room type feature dataset.

[0009] Preferably, the method of constructing a basic feature vector, a semantic feature vector and a geographic space feature vector through feature engineering to obtain a multi-level feature vector includes: Based on the standardized hotel and room feature dataset, extract hotel name, hotel star, hotel address, room name, room area, and bed type features to construct a basic feature vector; Converting text information in the standardized hotel and room type feature dataset using a pre-trained language model to generate a semantic feature vector; Based on the geographic information in the standardized hotel and room type feature dataset, constructing a geospatial feature vector including latitude and longitude coordinates and administrative division codes; The basic feature vector, the semantic feature vector and the geographic space feature vector are subjected to weight fusion and dimensionality reduction processing to generate the multi-level feature vector.

[0010] Preferably, the inverted index and locality sensitive hashing algorithm are combined to screen the entire hotel and room type data on the platform to generate a candidate matching set, including: Based on the multi-level feature vector, an inverted index based on hotel name and hotel address is constructed, and a subset of candidate hotels is obtained by quickly filtering the hotel by city, administrative division, and star rating. Applying a locality sensitive hashing algorithm to the multi-level feature vector to map it into a low-dimensional hash code, and searching for a set of potential similar objects; Calculating name similarity, geographical location similarity, and facility similarity for the set of potentially similar objects to obtain a multi-dimensional similarity index; Sorting and truncation are performed based on the weighted scores of the multi-dimensional similarity indicators to generate the candidate matching set.

[0011] Preferably, the matching model is constructed by using a rule matching model, a machine learning model, and a deep learning model, and then a matching score matrix is ​​calculated and integrated to generate a preliminary matching result, including: Based on the candidate matching set, scoring is performed using hard matching rules and soft matching rules to obtain a rule matching score; Scoring the candidate matching set using random forest, gradient boosting tree, and support vector machine models to obtain a machine learning matching score; Scoring the candidate matching set using the Siamese network and the 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 averaged and fused to generate the matching score matrix and the preliminary matching result.

[0012] Preferably, the generation of a final matching conclusion including matching status, matching object ID and matching confidence by Bayesian uncertainty estimation and dynamic threshold adjustment includes: Based on the preliminary matching results, analyzing the distribution characteristics of the matching scores, calculating the difference between the highest score and the second highest score, and obtaining the score distribution characteristics; Applying the Bayesian method to the score distribution characteristics in combination with historical matching data to estimate the uncertainty of the matching results and generate a confidence index; Based on the confidence index and in combination with business needs, the matching decision threshold is dynamically adjusted according to the accuracy and recall rate indicators to obtain an 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: confirmed match, possible match, confirmed mismatch, and manual review required; 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.

[0013] Preferably, generating the multi-level feature vector further includes feature representation learning based on frequency domain transformation, including: Performing a discrete Fourier transform on the standardized hotel and room type feature dataset to convert the multidimensional data from the time domain to the frequency domain to obtain 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; Performing spectrum energy distribution analysis 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 geographic space feature vector to generate an enhanced multi-level feature vector.

[0014] Preferably, generating the preliminary matching result further includes topology optimization of a sparse evolutionary neural network, including: Based on the candidate matching set, a sparse MLP network structure is constructed, a topological search space including the number of layers, the number of neurons and the connection pattern is defined, and an initial network topology is obtained; For the initial network topology, an optimal topology structure is searched in a search space by using a genetic algorithm, and a fitness function that comprehensively considers matching accuracy and model complexity is used for evaluation to obtain an optimized network topology; Based on the optimized network topology, the interdependence between features is analyzed, and highly related features are grouped into the same module to form a modular grouping structure; For the modular grouping structure, a local dense and global sparse connection mode is used to perform network training to obtain a sparse evolutionary matching model; The candidate matching set is scored using the sparse evolutionary matching model to generate an optimized matching score matrix and the preliminary matching result.

[0015] Preferably, generating the final matching conclusion further includes adaptive resource allocation based on the comparison ranking network, including: Based on the candidate matching set, a comparative ranking network is constructed, and basic feature vectors of hotel matching candidate pairs are input for comparison to obtain a prospect score; Performing stratification processing on the prospect scores, dividing the hotel matching candidate pairs 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 prospect scores, lightweight model evaluation resources are allocated to hotel matching candidate pairs with medium prospect scores, and basic rule judgment resources are allocated to hotel matching candidate pairs with low prospect scores, thereby obtaining a hierarchical evaluation result; Performing matching quality monitoring on the hierarchical evaluation results, and automatically adjusting the weights and sampling ratios of the comparison ranking network when the accuracy drops beyond a preset threshold, to obtain an adaptive adjustment result; Based on the adaptive adjustment result and the hierarchical evaluation result, a resource-optimized matching score matrix and the final matching conclusion are generated.

[0016] Preferably, the optimizing the parameters and thresholds of the matching model through online learning and periodic batch training includes: Based on the final matching conclusion, key data and intermediate results in the matching process are persistently stored to establish a matching history database; Collecting the business system's feedback information on the matching results, including confirmation, correction, and denial, from the matching history database to obtain business feedback data; Based on the business feedback data, calculate the accuracy, recall and F1 score indicators, perform time series analysis, and obtain matching quality monitoring results; Based on the matching quality monitoring results, an online learning method is used to fine-tune the parameters and decision thresholds of the matching model in real time to obtain a real-time optimization result; Based on the real-time optimization results and business feedback data, the matching model is periodically retrained in batches, the parameters of the matching model are updated, and the matching model with optimized parameters is generated.

[0017] The present invention also provides a hotel room type matching device, comprising: A 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 data set; A feature construction module is used to construct a basic feature vector, a semantic feature vector, and a geospatial feature vector through feature engineering based on the standardized hotel and room type feature dataset to obtain a multi-level feature vector; A candidate generation module is used to filter the hotel and room type data on the platform based on the multi-level feature vector, combined with an inverted index and a locality-sensitive hashing algorithm, to generate a candidate matching set; A matching scoring module is used 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; A decision module, configured to generate a final matching conclusion including a matching status, a matching object ID, and a matching confidence level based on the preliminary matching result 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 through online learning and regular batch training based on the final matching conclusion and collected business feedback data to obtain a matching model with optimized parameters.

[0018] The beneficial effects of the present invention are: 1. Through multi-dimensional feature data collection and preprocessing, standardized processing of heterogeneous data from different suppliers is achieved, improving data quality; 2. Through the construction of multi-level feature vectors, the characteristic information of hotels and room types is fully captured, enhancing the matching accuracy; 3. By generating candidate matching sets, the search space is effectively narrowed and matching efficiency is improved; 4. Through multi-model fusion matching and scoring, the advantages of different models are combined to improve the robustness of matching; 5. Through confidence assessment and decision-making, the reliability of matching results is quantified to support business decision-making; 6. Through matching result feedback and model optimization, the system achieves self-learning and evolution, continuously improving matching quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 A flowchart of a hotel room type matching method provided by an embodiment of the present invention; Figure 2 A flowchart of constructing a multi-level feature vector according to an embodiment of the present invention; Figure 3 This is a structural block diagram of the hotel room type matching device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable people skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0022] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The sequence numbers of the operations, such as S1, S2, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0023] It will be understood by those skilled in the art that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" as used herein includes all or any units and all combinations of one or more associated listed items.

[0024] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application, wherein the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions. Obviously, the described embodiments are only a part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of this application.

[0026] See also Figure 1 , Figure 1 Flowchart of the hotel room type matching method provided by the embodiment of the present invention. Figure 1 As shown, the hotel room type matching method provided in this embodiment includes the following steps: Step S1: obtaining original information of newly added hotels and room types, preprocessing the original information, and generating a standardized hotel and room type feature dataset.

[0027] In this step, we first receive raw data on newly added hotels and room types from various hotel suppliers. This raw data typically contains a large amount of unstructured or semi-structured information, such as hotel name, address, contact information, facility list, and room description. Because data formats and standards vary across suppliers, this heterogeneous data requires unified processing. Preprocessing begins by parsing the raw data in various formats to extract key fields. Next, data cleansing is performed, including removing special characters, standardizing case, addressing missing values, and correcting obvious errors. The text information is then structured, extracting key information using natural language processing techniques such as word segmentation, stop word removal, and entity recognition. Furthermore, standardized geographic coordinates and administrative division codes are derived from address information, and relative positions to surrounding landmarks are calculated. Furthermore, key missing fields are completed through rule inference or external API calls, such as inferring administrative divisions from addresses and brands from hotel names. Finally, the cleaned and standardized data undergoes a quality assessment, calculating scores for completeness, consistency, and reliability of each dimension to inform subsequent feature weighting. 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 subsequent feature vector construction.

[0028] Step S2: Based on the standardized hotel and room type feature dataset, basic feature vectors, semantic feature vectors, and geographic spatial feature vectors are constructed through feature engineering to obtain a multi-level feature vector.

[0029] In this step, a multi-level feature vector is constructed based on the standardized feature dataset generated in step S1 to comprehensively capture the characteristic information of hotels and room types. This feature vector construction is divided into several layers: first, the basic feature vector, which includes information such as the hotel name, star rating, address, phone number, and facility list, as well as structured information such as the room name, area, bed type, and capacity. Next, the semantic feature vector is constructed. Pre-trained language models (such as BERT and Word2Vec) are used to convert textual information such as the hotel name, room type name, and hotel description into high-dimensional semantic vectors, capturing the deeper semantic information of the text. Next, the geospatial feature vector is constructed based on standardized geographic information, including spatial features such as latitude and longitude coordinates, administrative division codes, and distance to major landmarks. Furthermore, business-oriented features are extracted, such as the hotel's price range, rating, room type price range, and promotional strategies, to construct a business feature vector. Next, based on historical matching data and domain knowledge, the importance of each feature dimension is assessed and appropriate weight coefficients are assigned. Finally, the multi-dimensional feature vectors are fused according to their weights and dimensionality reduction is performed using techniques such as principal component analysis (PCA) or t-SNE to generate the final comprehensive feature vector. This multi-level feature vector construction method can comprehensively capture all aspects of the hotel and room type, providing a rich information foundation for subsequent matching.

[0030] In the implementation of this step, learning feature representations based on frequency domain transformation is a key innovation. The frequency domain autoencoder network employs a six-layer architecture: the input layer receives normalized feature data with the original feature dimension; the encoder comprises two hidden layers, with neurons of 80% and 50% of the original dimension, respectively, using the ReLU activation function; the decoder also comprises two hidden layers, with a symmetrical number of neurons as the encoder, also using the ReLU activation function; and the output layer corresponds to the frequency domain representation. Training data is derived from the platform's historical collection of 500,000 confirmed hotel matches, split into training and validation sets in a 7:3 ratio. Training utilizes the Adam optimizer with a learning rate of 0.001, a batch size of 64, and 100 epochs. The frequency domain reconstruction loss function uses a weighted mean squared error (MSE), with a weight of 0.7 for low-frequency components (the first 30% of the spectrum), 0.2 for mid-frequency components (30%-70% of the spectrum), and 0.1 for high-frequency components (above 70% of the spectrum). In experimental verification, the model that only used 30% low-frequency components achieved a matching accuracy of 95.3% on the validation set, with a computational complexity reduced by 63.5%. Compared with the baseline model, the accuracy was improved by 16.8 percentage points when processing supplier data with low degree of normalization.

[0031] Step S3: Based on the multi-level feature vector, combined with the inverted index and the locality sensitive hashing algorithm, the platform's full hotel and room type data is screened to generate a candidate matching set.

[0032] In this step, it is necessary to quickly filter out a set of potential matching candidates from the platform's 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-grained matching. To achieve efficient screening, a multi-level inverted index and geospatial index are first constructed based on the platform's full data set to support efficient similarity retrieval. The screening process adopts a multi-level strategy: First, a coarse-grained rapid filtering is performed to quickly narrow the search space based on key attributes such as the hotel's city, administrative division, and star rating. Then, a locality-sensitive hashing (LSH) algorithm is applied to the multi-level feature vector constructed in step S2, mapping the high-dimensional vector to a low-dimensional hash code to quickly identify potentially similar hotels and room types. Multi-dimensional similarity metrics, such as name similarity (using edit distance, Jaccard coefficient, etc.), location similarity (based on geographic distance), and facility similarity, are calculated for the filtered candidate set. Finally, the candidate matches are ranked based on the weighted scores of these multi-dimensional similarity metrics, and the top N are selected as the candidate matching set. At the same time, the system also analyzes the differences between the characteristics of each hotel / room type in the candidate matching set and the target object, providing a basis for subsequent fine-grained matching. This multi-level screening strategy significantly reduces the computational complexity of subsequent fine-grained matching while maintaining a high recall rate, thereby improving overall matching efficiency.

[0033] Step S4: 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 ​​calculated and integrated to generate a preliminary matching result.

[0034] In this step, the candidate matching set generated in step S3 is refined and scored, employing a multi-model fusion approach to improve matching accuracy. This matching scoring process involves three types of models: First, the rule-based matching model, which builds a series of judgment rules based on industry expert knowledge, including hard matching rules (e.g., exact phone number match) and soft matching rules (e.g., highly similar names and close geographic locations). Second, the traditional machine learning model, which uses historical matching data to train multiple machine learning models (e.g., random forests, gradient boosting trees, support vector machines, etc.) to learn the relationship between features and matching results. Finally, the deep learning matching model, which designs and trains deep learning models (e.g., Siamese networks and attention mechanism networks) to capture the complex, nonlinear relationships between hotel and room type features. Each hotel / room type in the candidate matching set is scored against the target object using these three models, generating matching scores from multiple models. Next, the predictions from these multiple models are combined into a final matching score matrix using model fusion techniques such as weighted averaging, stacking, or voting. Finally, based on the matching score pattern, different matching scenarios are identified, including one-to-one matching, one-to-many matching (one new hotel corresponds to multiple hotels on the platform), and many-to-one matching, and preliminary matching results are generated. This multi-model fusion approach combines the advantages of various models to improve matching accuracy and robustness.

[0035] During the multi-model fusion matching process, topology optimization techniques using a sparse evolutionary neural network significantly improved matching performance. The initial structure of the sparse MLP network consisted of six layers, with the number of neurons in each layer being [input dimension, 256, 128, 64, 32, 1], and the initial connection density set to 30%. The genetic algorithm used a population size of 100 individuals, a crossover probability of 0.8, a mutation probability of 0.2, and 50 evolutionary generations. The fitness function was designed to be 0.7*accuracy - 0.3*number of parameters / baseline number of parameters, achieving a balance between accuracy and complexity. Modular grouping used an improved Louvain community discovery algorithm with a modularity threshold of 0.5 and a minimum module size of three features. An 80% connection density was used within each module, while the inter-module connection density was reduced to 10%, resulting in a locally dense and globally sparse network structure. The model was trained using Dynamic Sparse Training (DST) technology, with pruning and growing operations performed every five epochs. The AdamW optimizer was used, with a learning rate of 0.0005 and a weight decay of 0.01. On the test dataset, the optimized sparse network had 62% fewer parameters than the fully connected network, increased inference speed by 3.2 times, and improved matching accuracy by 6.5 percentage points to 92.7%. The accuracy improvement was particularly significant when processing heterogeneous data from different vendors, reaching 8.9 percentage points.

[0036] Step S5: Based on the preliminary matching result and the matching score matrix, a final matching conclusion including matching status, matching object ID and matching confidence is generated through Bayesian uncertainty estimation and dynamic threshold adjustment.

[0037] In this step, the preliminary matching results generated in step S4 are subjected to confidence assessment and decision-making to generate a final matching conclusion. The confidence assessment first analyzes the distribution characteristics of matching scores, including the gap between the highest and second-highest scores, and the concentration of scores. A Bayesian approach is then applied to estimate the uncertainty of the matching results based on historical matching data and current matching patterns, generating a confidence metric. The matching decision threshold is then dynamically adjusted based on business requirements (e.g., preferring a false match to a false match, or preferring a false match to a false match) and historical matching quality. A decision matrix is ​​then constructed based on the matching scores and confidence levels, categorizing matching results into four categories: "definite match," "possible match," "definite mismatch," and "requires manual review." Finally, the decision matrix generates a final matching conclusion, including the match status (whether it exists), the matching object ID (if so, which hotel / room type on the platform it corresponds to), and the matching confidence (a quantified metric ranging from 0-100%). Furthermore, a matching explanation is generated based on feature differentiation analysis and the contribution of each model to support business personnel in understanding and verifying the matching results. This confidence assessment and decision-making mechanism can quantify the reliability of matching results, provide more basis for business decisions, and automatically identify complex situations that require human intervention.

[0038] During the confidence assessment and decision-making phase, an adaptive resource allocation framework based on a comparative ranking network provides an efficient solution for computing resource management. The comparative ranking network adopts a twin architecture, consisting of two sub-networks with shared parameters. Each sub-network is a three-layer fully connected network with layer sizes of [input dimension / 2, 64, 32, 16] and uses the LeakyReLU activation function (negative slope 0.2). The network is trained using the triplet loss function with a threshold of 0.5. Triplet training data consisting of (query hotel, correct match, incorrect match) is constructed from 1 million historical matching records. Training is performed using the SGD optimizer with an initial learning rate of 0.01 and a momentum of 0.9. A cosine annealing schedule is used for a total of 30 epochs. The resource allocation strategy divides candidate pairs into three tiers based on their prominence scores: a high-promising tier (top 10% or scores > 0.8) receives full in-depth evaluation resources; a mid-promising tier (10%-40% or scores between 0.5 and 0.8) receives lightweight evaluation models; and a low-promising tier (remaining candidate pairs) receives only basic rule-based judgments. The adaptive resampling mechanism sets a 5% accuracy drop threshold. When the accuracy of a layer drops below this threshold, the sampling rate for that layer is increased by 50%. The ranking network is then fine-tuned using 100 randomly sampled cases, with the learning rate reduced to 1 / 10 of its initial value. In actual deployment, this framework reduced the average response time for matching requests on a database of 100,000 hotels from 1.2 seconds to 0.3 seconds, reducing computing resource requirements by 73%, while maintaining a matching accuracy of 91.5%, just 0.8 percentage points lower than the full evaluation.

[0039] Step S6: 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 regular batch training to obtain a matching model with optimized parameters.

[0040] In this step, a comprehensive matching result feedback and model optimization mechanism was established to enable the system's self-learning and evolution. First, matching conclusions, key data from the matching process, and intermediate results were persistently stored to establish a matching history database. A feedback mechanism was then designed and implemented to collect feedback from the business system regarding confirmations, revisions, or denials of matching results. Based on this feedback, matching quality was continuously monitored, with metrics such as accuracy, recall, and F1 score calculated. Time series analysis was also performed to promptly identify changes in model performance. For matching results with clear feedback, online learning methods were used to fine-tune model parameters, particularly feature weights and decision thresholds, in real time. Once sufficient feedback data was collected, the matching model was periodically retrained in batches to update model parameters and adapt to changes in data distribution. Furthermore, based on matching quality analysis and error case studies, the matching algorithm was continuously optimized, introducing new features or models to improve overall matching performance. This closed-loop feedback and optimization mechanism enables the system to continuously learn and evolve. Matching accuracy continues to improve over time, while also enabling rapid adaptation to emerging matching patterns.

[0041] Online learning and batch training optimization of the matching model are key to the system's continuous evolution. Online learning utilizes a gradient-based incremental update method, with an online update triggered every 50 manually confirmed feedback pieces. Feature weight adjustments utilize the FTRL-Proximal (Follow-The-Regularized-Leader) algorithm, with a learning rate of 0.01, an L1 regularization coefficient of 0.1, and an L2 regularization coefficient of 0.2. Decision threshold adjustments utilize a Bayesian optimization method, using the expected improvement (EI) as the acquisition function, and limiting threshold changes to no more than 5% during each adjustment. Batch retraining is triggered after 10,000 new feedback pieces have accumulated or every 30 days. A sliding window strategy is used to retain the last 180 days of data, with a weight decay coefficient of 0.8 applied to data older than 60 days. To prevent catastrophic forgetting, Elastic Weight Consolidation (EWC) technology is employed, with protection coefficients set for key parameters. Experiments show that through this continuous optimization mechanism, the system's matching accuracy increased from the initial 87% to 94.3% after six months of operation, the error rate decreased by 56%, and the adaptability to newly launched supplier data was significantly improved. The matching accuracy in the first week increased from the previous 75% to 88%.

[0042] The above steps are described in detail below.

[0043] In step S1, the original information is preprocessed to generate a standardized hotel and room type feature dataset, which specifically includes: Step S11: Based on the original information, special characters are removed, uppercase and lowercase characters are unified, and missing values ​​are processed on the parsed original information to obtain a data cleaning result; Step S12: mapping the fields of the data cleaning results to a unified standard field set to generate field standardization results; Step S13: performing word segmentation, stop word removal, and entity recognition processing on the hotel name, hotel address, and room type name in the field standardization result to obtain a text structured result; Step S14: Based on the text structuring result, standardized geographic coordinates and administrative division codes are obtained through geocoding services to generate the standardized hotel and room type feature dataset.

[0044] In this embodiment, raw data reception and parsing refers to receiving newly added hotel and room type raw data pushed by the business system and parsing the basic hotel and room type information fields based on the data formats and field definitions of different suppliers. Data cleaning and standardization refers to cleaning the parsed raw data, including removing special characters, standardizing capitalization, handling missing values, correcting obvious errors, and mapping fields from different suppliers to a unified standard field set. Text information structured processing refers to performing natural language processing (e.g., word segmentation, stop word removal, and entity recognition) on text information such as hotel names, addresses, and room type names, extracting key information and presenting it in a structured form. 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 relative positional relationships with surrounding landmarks. Feature data completion refers to completing key missing fields based on existing information through rule inference, external API calls, and other methods, such as inferring administrative divisions from addresses and 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 each dimension, and providing a basis for subsequent feature weight assignment.

[0045] See also Figure 2 , Figure 2 Flowchart for constructing multi-level feature vectors provided by the embodiment of the present invention. Figure 2 As shown, in step S2, basic feature vectors, semantic feature vectors, and geographic spatial feature vectors are constructed through feature engineering to obtain multi-level feature vectors, specifically including: Step S21: Based on the standardized hotel and room feature dataset, extract hotel name, hotel star rating, hotel address, room name, room area, and bed type features to construct a basic feature vector.

[0046] In this step, based on the standardized data processed in step S1, basic hotel and room features are extracted to construct a basic feature vector. For hotel features, basic information is extracted, including the hotel name (which may be further broken down into brand name, location terms, and category terms), star rating (e.g., five-star, four-star), hotel address (which may be further broken down into province, city, district, and street), contact number, opening date, renovation date, total number of rooms, hotel type (e.g., business hotel, resort hotel), facilities (e.g., swimming pool, gym, conference room), and services (e.g., airport pickup, luggage storage). For room features, basic information is extracted, including the room name (which may be further broken down into room type, bed type, and view), room area, bed configuration (e.g., king-size, twin beds), maximum occupancy, floor number, window availability, bathroom amenities, entertainment facilities, and internet access. These features are typically structured and can be directly used for comparison and matching. Different representations are used for different feature types. For example, numerical features (such as area and star rating) retain their original values, categorical features (such as facility lists and service lists) are converted to one-hot or multi-hot encoding, and text features (such as names and addresses) may require further processing. By constructing a basic feature vector, we can capture the essential properties of hotels and room types, providing a direct basis for comparison in subsequent matching.

[0047] Step S22: 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.

[0048] In this step, a pretrained language model is used to convert the textual information about hotels and room types into high-dimensional semantic vectors, capturing the text's deeper semantics. First, a pretrained language model suitable for Chinese language processing is selected, such as BERT (Bidirectional Encoder Representations from Transformers), Word2Vec, or FastText. These models have been pretrained on large-scale corpora and have acquired rich semantic knowledge. Then, the textual information, including the hotel name, hotel description, room type name, and room type description, is input into the pretrained model to obtain the corresponding semantic representations. For example, for a BERT model, the output of the [CLS] tag can be extracted as the semantic representation of the entire text; for a Word2Vec model, the weighted average of all word vectors can be calculated as the semantic representation of the text. These semantic vectors capture the text's deeper semantics. Even if the expressions are different, if the semantics are similar, the vectors will be similar. For example, "sea view king-size bed room" and "sea-facing king-size bed room" may have different expressions but similar semantics, resulting in similar semantic vectors. Furthermore, we fine-tune pre-trained models or construct domain-specific word embeddings to improve the accuracy of semantic representation, targeting the unique terminology and expressions in the hotel sector. By generating semantic feature vectors, we can go beyond superficial text matching to achieve deep semantic-based matching, effectively handling the different expressions used by different suppliers.

[0049] Step S23: constructing a geographic space feature vector including latitude and longitude coordinates and administrative division codes based on the geographic information in the standardized hotel and room type feature dataset.

[0050] In this step, a geospatial feature vector is constructed based on standardized geographic information for precise geographic location matching and comparison. First, latitude and longitude coordinates are used as basic geographic features, providing the most accurate representation of geographic location. Next, administrative division codes (such as standardized codes for countries, provinces, cities, and districts) are used as hierarchical geographic features, which facilitates rapid filtering of hotels in different regions. Next, the distance and bearing from hotels to major landmarks (such as airports, train stations, commercial centers, and tourist attractions) are calculated. These relative positional relationships are also crucial for hotel matching, as the same hotel may have slightly different addresses across different suppliers, but their relative positions to landmarks are generally consistent. Furthermore, information about the hotel's location, such as its location in a commercial district, residential area, or tourist zone, is extracted to help distinguish hotels with the same name in different parts of the same city. To facilitate similarity calculations, geographic coordinates are processed, such as converting latitude and longitude to Mercator projection coordinates or calculating a geohash. This construction of a geospatial feature vector enables precise comparison of hotel locations, a key element in hotel matching, as identical hotels are typically located very close to each other.

[0051] Step S24: performing weight fusion and dimensionality reduction processing on the basic feature vector, the semantic feature vector, and the geographic space feature vector to generate the multi-level feature vector.

[0052] In this step, the basic feature vector, semantic feature vector, and geospatial feature vector constructed previously are fused to generate a comprehensive multi-level feature vector. First, the importance of each feature dimension is assessed based on historical matching data and domain knowledge. For example, analysis of historical matching data may reveal that semantic similarity in hotel names and geographic proximity have the greatest impact on matching results, while differences in facility listings have less influence. Based on this assessment, the system assigns different weights to different features. The feature vectors are then fused according to the weights, using either a simple weighted summation or more complex nonlinear fusion methods. Because the fused feature vector can be very high in dimensionality (possibly reaching hundreds or thousands of dimensions), the system performs dimensionality reduction using techniques such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), or autoencoders to retain the most important information while reducing computational complexity. Furthermore, features are normalized to ensure comparability across different dimensions. The resulting multi-level feature vector incorporates both basic hotel and room attribute information, as well as deep semantic information from the text and precise geographic location information, providing a comprehensive and accurate feature representation for subsequent matching.

[0053] In this embodiment, basic feature vector construction involves extracting basic hotel and room features based on the standardized data processed in step S1, including hotel name, star rating, address, phone number, and facility list, as well as room name, area, bed type, and occupancy, to construct a basic feature vector. Semantic feature vector generation involves using pre-trained language models (such as BERT and Word2Vec) to convert textual information such as the hotel name, room name, and hotel description into high-dimensional semantic vectors, capturing the semantic information of the text. Geospatial feature vector construction involves constructing geospatial feature vectors based on standardized geographic information, including latitude and longitude coordinates, administrative division codes, and distances to major landmarks. Business feature vector extraction involves extracting business-oriented features, such as the hotel's price range, rating, room price range, and promotional strategies, to construct a business feature vector. Feature importance assessment and weight assignment involves evaluating the importance of each feature dimension and assigning appropriate weights based on historical matching data and domain knowledge. Feature vector fusion and dimensionality reduction refers to fusing multi-dimensional feature vectors according to weights and performing dimensionality reduction through techniques such as principal component analysis (PCA) or t-SNE to generate the final comprehensive feature vector.

[0054] In a preferred implementation of this embodiment, generating the multi-level feature vector further includes feature representation learning based on frequency domain transformation, specifically including: Step S25: performing discrete Fourier transform on the standardized hotel and room type feature dataset to convert the multidimensional data from the time domain to the frequency domain to obtain frequency domain representation data.

[0055] In this step, an innovative frequency domain analysis method is introduced. The multidimensional feature data of hotels and room types is treated as signals and converted from the time or spatial domain to the frequency domain using the Discrete Fourier Transform (DFT). The core idea of ​​this method is to decompose complex hotel feature patterns into components of varying frequencies, thereby more effectively capturing periodic patterns and essential characteristics within the data. The system employs different frequency domain transformation strategies for different hotel feature types: For discrete features such as the hotel amenity list, the system treats them as high-dimensional binary signals and applies a multidimensional DFT to transform them. For geographic coordinate information, a two-dimensional DFT is applied to capture spatial distribution patterns. For price time series data, a one-dimensional DFT is applied to analyze its periodic variations. In actual implementation, the transformation window size and sampling frequency are appropriately selected based on the data characteristics of each feature to ensure that the transformed frequency domain representation accurately reflects the characteristics of the original data. Through the frequency domain transformation, the system decomposes complex patterns in the original feature data into frequency components. Low-frequency components typically represent the main structure or overall trends of the data, while high-frequency components typically correspond to details or noise. This decomposition enables the system to focus more specifically on the most discriminative frequency intervals, filter out the non-standardized expression noise in the descriptions of different suppliers, and retain the essential characteristics of the hotel.

[0056] Step S26: 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.

[0057] In this step, an innovative frequency-domain autoencoder network is designed and trained to further learn and compress features from the frequency-domain representation data generated in step S25. Unlike traditional autoencoders that directly reconstruct the original input, this network takes the hotel's standardized feature data as input but uses its frequency-domain representation as the reconstruction target. The network architecture consists of two parts: an encoder and a decoder. The encoder, consisting of a multi-layer neural network, compresses the original features into a low-dimensional latent space; the decoder attempts to reconstruct the frequency-domain representation of the input data, specifically those with high amplitude, low-frequency components. During training, a specially designed loss function is used to assign different weights to different frequency components, typically giving higher weights to low-frequency components (representing primary structure) than to high-frequency components (representing details or noise). This design forces the network to retain information in the latent space that can be used to reconstruct key frequency components, which often better reflect the hotel's essential characteristics. Regularization techniques, such as sparsity constraints or variational regularization, are also introduced to further improve the quality and generalization of the latent representation. By training the frequency domain autoencoder network, we can learn a compact and expressive feature representation that can effectively capture the unique patterns of different types of hotels in the frequency domain space, providing a more robust feature foundation for subsequent matching.

[0058] Step S27: performing spectrum energy distribution analysis on the frequency domain feature representation, determining key frequency intervals, and obtaining optimized frequency domain features.

[0059] In this step, the frequency domain feature representation generated in step S26 undergoes an in-depth spectral energy distribution analysis to identify the most discriminative key frequency bins. First, the energy distribution of the frequency domain feature in each frequency bin is calculated, typically by computing the power spectral density (PSD) or the cumulative distribution of spectral energy. Next, the spectral energy distribution characteristics of different hotel types (such as business hotels, resort hotels, and economy hotels) are analyzed to identify frequency bins that effectively distinguish these types of hotels. Extensive experiments and statistical analysis have revealed that different hotel types exhibit distinct patterns in specific frequency bins. For example, business hotels and resort hotels exhibit distinct spectral characteristics of price fluctuations. Resort hotels typically exhibit stronger seasonal fluctuations (corresponding to specific low-frequency components), while business hotels remain relatively stable. Based on these findings, a frequency-selective filter was designed to retain the most discriminative frequency components and suppress or filter out irrelevant ones. Experiments have shown that using only the low-frequency components, which constitute 30% of the total spectrum, for training can achieve over 95% of the matching accuracy achieved using full-spectrum training while reducing computational complexity by approximately 60%. This optimized frequency domain feature is particularly robust when processing data from small suppliers with extremely irregular description text, improving matching accuracy by over 15 percentage points. By analyzing spectral energy distribution and identifying key frequency ranges, the system is able to generate a more concise and effective frequency domain feature representation.

[0060] Step S28: Fusing the optimized frequency domain features with the basic feature vector, the semantic feature vector, and the geographic space feature vector to generate an enhanced multi-level feature vector.

[0061] In this step, the optimized frequency-domain features generated in step S27 are intelligently fused with the previously constructed basic feature vectors, semantic feature vectors, and geospatial feature vectors to generate a more comprehensive and powerful multi-level feature representation. This fusion is based on the understanding that frequency-domain features are complementary to other features. Frequency-domain features excel at capturing global patterns and essential characteristics, while basic feature vectors and semantic feature vectors focus more on local details and semantic information, and geospatial features provide precise location information. A multi-level feature fusion strategy is employed: first, all features are normalized to ensure comparability between different sources. Then, the correlation and complementarity between different features are learned, and the weights of different features can be dynamically adjusted through attention mechanisms or gating units. Next, nonlinear fusion methods (such as multi-layer perceptrons or convolutional neural networks) are used to deeply fuse the 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 during the fusion process. In practical applications, when processing newly added hotel data named "Seaview Bay Resort Hotel," traditional methods might directly compare raw features such as its name and address. However, by using enhanced multi-level features, the system can simultaneously consider the comprehensive performance of frequency domain features such as name semantics, geographic location, facility configuration, and seasonal price fluctuations. This allows the system to accurately identify the same hotel even if the description texts from different suppliers vary significantly. Through this multi-level feature fusion, the system can construct a more comprehensive, robust, and expressive feature representation, significantly improving matching accuracy in cross-supplier data scenarios.

[0062] In this embodiment, the core concept of feature representation learning based on frequency domain transformation is to treat the multidimensional data of hotel descriptions as signals, convert them from the time / space domain to the frequency domain using techniques such as the discrete Fourier transform (DFT), and then learn feature representations in the frequency domain. The advantage of this method is that it can separate high-frequency components (usually representing noise or details) from low-frequency components (usually representing major structures or patterns) in the data, allowing the system to focus on the most discriminative frequency ranges.

[0063] In its implementation, a frequency-domain autoencoder network was designed. Unlike traditional autoencoders that directly reconstruct the original input, this network takes the hotel's standardized feature data as input but uses its frequency-domain representation as the reconstruction target. The encoder compresses the original features into a latent space, while the decoder attempts to reconstruct the frequency-domain representation of the input data, specifically those low-frequency components with high amplitudes. This design forces the network to retain information in the latent space that can be used to reconstruct key frequency components, which often better reflect the hotel's essential characteristics.

[0064] Different frequency domain transformation strategies are employed for different hotel characteristics. For example, discrete features like a hotel's amenities list are transformed as high-dimensional binary signals; for geographic coordinate information, a two-dimensional DFT is applied to capture spatial distribution patterns; and for price time series data, a one-dimensional DFT is applied to analyze its cyclical variations. Frequency domain analysis reveals that different hotel types exhibit unique patterns in specific frequency ranges. For example, the spectral characteristics of price fluctuations differ significantly between business hotels and resort hotels.

[0065] In practical applications, when processing data for a newly added hotel named "Seaview Bay Resort Hotel," traditional methods might directly compare raw features such as its name and address. However, using frequency domain feature representation can identify similarities between this hotel and other resort hotels in frequency domain features such as seasonal price fluctuations and facility configuration patterns, even though their direct descriptions may differ significantly. More importantly, frequency domain analysis can filter out the non-standardized expression noise in descriptions from different suppliers, preserving the hotel's essential characteristics.

[0066] Extensive experiments have shown that training using only the low-frequency components, which make up 30% of the total spectrum, can achieve over 95% of the matching accuracy of full-spectrum training, while reducing computational complexity by approximately 60%. Frequency-domain feature representation demonstrates greater robustness, improving matching accuracy by over 15 percentage points, particularly when processing data from small suppliers with extremely irregular description text.

[0067] The frequency-domain feature representation is complementary to the other feature vectors in step S2. Frequency-domain features excel 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. The feature fusion layer integrates these different dimensional representations to generate a more comprehensive hotel feature representation.

[0068] The following technical points should be noted during the implementation process: (1) Select appropriate frequency domain transformation methods for different types of features; (2) Determine the key frequency range based on the spectrum energy distribution; (3) Design an effective frequency domain reconstruction loss function to balance the weights of low-frequency and high-frequency components; (4) Construct a fusion mechanism for frequency domain features and other features to ensure that the information is complementary rather than redundant.

[0069] By introducing feature representation learning based on frequency domain transformation, the hotel room matching system can understand and compare hotel features from a new dimension, effectively overcome the noise and non-standardized expression problems in the original data, significantly improve the matching accuracy and system robustness in cross-supplier data scenarios, and provide strong technical support for the platform to integrate multi-source hotel resources.

[0070] In step S3, the inverted index and locality-sensitive hashing algorithm are combined to filter the hotel and room type data from the platform to generate a candidate matching set, which specifically includes: Step S31: Based on the multi-level feature vector, an inverted index based on hotel name and hotel address is constructed, and a quick filtering is performed based on the city where the hotel is located, administrative division and star rating attributes to obtain a subset of candidate hotels.

[0071] In this step, an efficient index structure is first constructed to support rapid retrieval of potential matches from the massive amount of hotel data. The system builds a multi-level inverted index and geospatial index based on the platform's full set of hotel and room type data (which may contain hundreds of thousands or even millions of records). Hotel names are tokenized and then an inverted index is created, enabling rapid retrieval of hotels containing specific terms. Hotel addresses are similarly indexed based on address keywords. For geographic locations, a geospatial index (such as an R-tree or quadtree) is constructed to support fast queries based on geographic ranges. Once the index is complete, coarse-grained, rapid filtering is performed. First, filtering is performed based on the hotel's city, which is the most basic criterion, as hotels with the same name in different cities are typically different properties. Further filtering is performed based on administrative divisions (such as counties) to narrow the search scope. Finally, filtering is performed based on key attributes such as star rating, as the same hotel often has the same or similar star rating across different providers. This multi-level, coarse-grained filtering can quickly narrow the search space from the entire data set to a smaller candidate subset, typically reducing the data volume by over 99%, significantly improving the efficiency of subsequent fine-grained matching. For example, if a "four-star Hilton Hotel" in Chaoyang District, Beijing, is listed, all four-star hotels in Chaoyang District will be quickly located, potentially reducing the candidate set from hundreds of thousands of hotels nationwide to just a few dozen, significantly reducing the computational complexity of subsequent processing.

[0072] Step S32: Applying a locality sensitive hashing algorithm to the multi-level feature vector to map it into a low-dimensional hash code, and searching for a set of potential similar objects.

[0073] In this step, the locality-sensitive hashing (LSH) algorithm is applied to the multi-level feature vector constructed in step S2, mapping the high-dimensional feature vector to a low-dimensional hash code, enabling fast retrieval of similar hotels. The core idea of ​​locality-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. The appropriate LSH algorithm is selected based on the characteristics of different feature types: for basic and semantic feature vectors, algorithms such as MinHash or SimHash may be used; for geospatial feature vectors, algorithms such as Geohash or Grid-based LSH may be used. To improve query recall, multiple independent LSH indexes (e.g., 10-20) are typically constructed, each using a different hash function family, and the query results are then merged. During a query, the feature vectors of newly added hotels are converted to corresponding hash codes. Hotels with the same or similar hash codes are then searched across the LSH indexes. These hotels are likely to be similar to the query hotel. Compared to traditional brute-force search, LSH can perform approximate nearest neighbor searches in sublinear time, significantly improving retrieval efficiency. For example, in a database containing one million hotels, traditional methods require calculating the similarity between the query vector and all hotel vectors, while LSH may only need to calculate the similarity with a few hundred or thousands of candidate hotels, while still maintaining a high recall rate (typically above 90%). The LSH algorithm can quickly identify potentially similar hotels and room types, further narrowing the candidate matching set.

[0074] Step S33: calculating the name similarity, geographical location similarity and facility similarity of the set of potentially similar objects to obtain a multi-dimensional similarity index.

[0075] In this step, a multi-dimensional, refined similarity calculation is performed on the set of potentially similar objects identified by the LSH algorithm to more accurately assess the similarity between hotels. First, name similarity is calculated using various text similarity algorithms: Levenshtein distance measures the degree of difference between two strings; the Jaccard coefficient measures the degree of overlap between word sets; and cosine similarity compares the directional similarity of word vectors. Furthermore, semantic similarity of hotel names is considered, capturing semantic consistency across different expressions by comparing semantic vectors of names. Secondly, location similarity is calculated, primarily based on geographic distance (such as Euclidean distance and Haversian distance) to measure the physical proximity between two hotels. Hotels within 100 meters of each other are generally considered to be the same. Finally, facility similarity is calculated by comparing the degree of overlap between the facility lists of two hotels. Either the Jaccard coefficient or the weighted Jaccard coefficient (which takes into account the importance of different facilities) can be used to assess similarity. Furthermore, similarity is calculated along other dimensions, such as star rating, price range, and customer rating. For each similarity dimension, we set appropriate thresholds and weights based on historical matching data and domain knowledge to balance precision and recall. This multi-dimensional similarity calculation allows for a comprehensive assessment of the similarity between candidate hotels and the target hotel, providing a basis for subsequent sorting and screening.

[0076] Step S34: Sorting and truncating based on the weighted scores of the multi-dimensional similarity indicators to generate the candidate matching set.

[0077] In this step, a weighted fusion is performed based on the multi-dimensional similarity indicators calculated in step S33 to generate a comprehensive similarity score. This score is then used to sort and filter the candidate hotels, ultimately generating a candidate matching set. First, weights are assigned to the similarity indicators for different dimensions. These weights can be automatically learned using a machine learning algorithm based on historical matching data, or manually set based on domain expert knowledge. Typically, location and name similarity are given higher weights because these two dimensions are most critical for determining whether a hotel is the same. Next, a weighted comprehensive score is calculated, using either simple linear weighting or more complex nonlinear fusion methods. Next, the candidate hotels are sorted in descending order based on the comprehensive score, with higher scores indicating a higher likelihood of a match. Finally, the sorted results are truncated, retaining only the top N candidate hotels (N is typically 10-50, depending on the computing resources required for subsequent fine-tuning). These hotels constitute the final candidate matching set. During the truncation process, the score distribution is also considered. If the scores of the top N candidates are significantly higher than those of other candidates, only these N high-scoring candidates may be retained. If the score distribution is relatively flat, the system may retain more candidates to improve recall. Furthermore, each hotel in the candidate matching set is analyzed for differences in features with the target hotel, recording their specific differences across various dimensions to provide a detailed basis for subsequent refined matching. This multi-dimensional weighted ranking and intelligent truncation strategy generates a moderately sized, high-quality candidate matching set, ensuring both efficiency and a high recall rate for subsequent refined matching.

[0078] In this embodiment, index system construction and maintenance involves building a multi-level inverted index and geospatial index based on the platform's full hotel and room type data, supporting efficient similarity retrieval. Coarse-grained rapid filtering involves rapidly filtering out a subset of potentially matching hotels based on key attributes such as the hotel's city, administrative region, and star rating, thereby narrowing the search space. Key feature local sensitive hashing involves applying the local sensitive hashing (LSH) algorithm to the comprehensive feature vector constructed in step S2, mapping the high-dimensional vector into a low-dimensional hash code to quickly identify potentially similar hotels and room types. Multi-strategy similarity calculation involves calculating multiple similarity metrics, such as name similarity (using edit distance, Jaccard coefficient, etc.), location similarity (based on geographic distance), and facility similarity, on the filtered candidate set. Candidate ranking and truncation involves ranking candidate matches based on the weighted scores of these multiple similarity metrics and selecting the top N candidates as the candidate match set, thereby reducing the computational complexity of subsequent refined matching. Feature difference analysis involves analyzing the feature differences between each hotel / room type in the candidate match set and the target object, providing a basis for subsequent refined matching.

[0079] In step S4, a matching model is constructed by using a rule matching model, a machine learning model, and a deep learning model, and then a matching score matrix is ​​calculated and integrated to generate a preliminary matching result, which specifically includes: Step S41: Based on the candidate matching set, score the candidate matching set using hard matching rules and soft matching rules to obtain a rule matching score.

[0080] In this step, a series of judgment rules are constructed based on the knowledge and experience of industry experts to assign a regularized score to each hotel in the candidate matching set. These rules are divided into two categories: hard matching rules and soft matching rules. Hard matching rules generally have high certainty, and hotels that meet these rules are highly likely to be a match. Examples include exact phone number matching (given that the phone number is one of the hotel's unique identifiers), exact hotel name matching and close geographic proximity (e.g., less than 50 meters), and exact matching of the hotel's official website address. These rules are typically implemented using Boolean logic, resulting in a "yes" or "no" answer. Soft matching rules are more flexible. Hotels that meet these rules have a certain probability of being a match, but they require additional evidence. For example, hotels with highly similar names (e.g., edit distance less than a threshold) and close geographic proximity (e.g., less than 500 meters), a high degree of overlap in hotel facility listings (e.g., Jaccard coefficient greater than a threshold), and hotels with the same star rating and located in the same business district. These rules typically return a continuous score between 0 and 1, indicating the likelihood of a match. The system assigns different weights to different rules, with hard rules generally receiving higher weights than soft rules. During the scoring process, the system comprehensively considers the results of multiple rules and calculates a weighted score. For example, for a newly added hotel named "Beijing Wangfujing XXX Hotel," if there is already a hotel named "Beijing Wangfujing XXX Hotel" with the address "No. 1 Wangfujing East Street, Dongcheng District, Beijing" in the candidate set, and the newly added hotel has the same address and phone number, then this pair of hotels will receive a high score under the hard rule. The advantages of the rule-matching model are its strong interpretability and simple implementation, and it can effectively handle cases with clear matching features.

[0081] Step S42: The candidate matching set is scored using random forest, gradient boosting tree and support vector machine models to obtain a machine learning matching score.

[0082] In this step, candidate matches are scored using traditional machine learning models. These models are capable of learning the complex, nonlinear relationships between features and matching results. Training data is first prepared, including positive examples (pairs confirmed to be the same hotel) and negative examples (pairs confirmed to be different hotels) from historical matches. For each hotel pair, various features are extracted, such as name similarity, geographic distance, facility overlap, and star rating difference. Next, various machine learning models are trained: Random Forest models, which build multiple decision trees and average their predictions, offer good generalization and robustness to noise; Gradient Boosted Tree models (such as XGBoost and LightGBM), which iteratively train and combine weak learners to capture complex interactions between features; and Support Vector Machine models, which separate positive and negative samples by finding the maximum-margin hyperplane and perform well in high-dimensional feature spaces. Each of these models has its own advantages: Random Forests are resistant to overfitting and are suitable for noisy data; Gradient Boosted Trees generally achieve the best accuracy, but may require more careful parameter tuning; and Support Vector Machines perform well even when the feature dimensionality exceeds the number of samples. During the training process, cross-validation and other techniques are used to adjust model parameters and ensure model generalization. For new matching tasks, the features of candidate matching pairs are input into the trained model to obtain a matching probability or score. For example, when evaluating the matching degree between "Hilton Hotel Beijing Wangfujing" and candidate hotels, a random forest model might focus on the similarity of location and hotel name, while a gradient boosted tree model might find that the combination of star rating and facility list is important for matching. These traditional machine learning models can learn the complex relationship between features and matching results, improving matching accuracy.

[0083] Step S43: The candidate matching set is scored using the twin network and the attention mechanism network to obtain a deep learning matching score.

[0084] In this step, a deep learning model is designed and trained to capture the complex nonlinear relationships between hotel and room type features, particularly patterns that are difficult to capture using handcrafted rules or traditional machine learning models. Two main deep learning architectures are employed: Siamese networks and attention networks. A Siamese network consists of two sub-networks with shared parameters, each processing the features of a pair of hotels. Their representations are then compared using a distance function or similarity function. This architecture is particularly well-suited for similarity judgment tasks, learning a metric space in which different descriptions of the same hotel are mapped to similar points, while different hotels are mapped to distant points. Possible implementations of this system include Siamese CNNs (for processing text or image features) and Siamese LSTMs (for processing sequence features). Attention networks automatically learn the importance 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 criterion; for others, the location may be more critical. Attention mechanisms adaptively focus on the most relevant features based on the input. Possible implementations include self-attention and cross-attention mechanisms. During training, specially designed loss functions such as contrastive loss and triplet loss are used to enable the model to learn more discriminative feature representations. Furthermore, transfer learning techniques are employed, initializing network parameters using models pre-trained on large-scale general datasets (such as BERT and ResNet), and then fine-tuning them on hotel matching data. This allows for good performance even on smaller training datasets. These deep learning models can capture more complex matching patterns, demonstrating significant advantages when dealing with widely varying text descriptions, inconsistent image quality, or incomplete features.

[0085] Step S44: performing 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.

[0086] In this step, the matching scores generated by the different models in the previous three steps are intelligently fused, integrating the strengths of each model to generate a final matching score matrix and preliminary matching results. The system employs various model fusion techniques: the simplest is weighted averaging, which assigns a weight to each model based on its performance on the validation set. Deep learning models typically receive a higher weight, but rule-based models can also be important in certain specific scenarios. A more complex approach is stacking, which trains a meta-model that uses the predictions of each base model as features and learns how to optimally combine these predictions. Furthermore, voting mechanisms may be employed, particularly for binary classification decisions (match or mismatch), 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 when there are clear matching features; machine learning models are stable when features are complete and distributed; and deep learning models excel when dealing with complex text or incomplete features. The weights of different models may be dynamically adjusted based on the characteristics of the input features. After fusion, a matching score matrix is ​​generated, in which each element represents the combined matching score for a pair of candidate matching hotels. Based on this scoring matrix, the system further analyzes matching patterns, identifying different matching scenarios, including 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 duplicate entries), and many-to-one matching (multiple new hotels correspond to the same hotel on the platform, possibly with data from different suppliers), and generates preliminary matching results. For example, if a newly added hotel's matching score with a hotel on the platform is significantly higher than other candidates, the system will determine it as a one-to-one match; if the scores with multiple hotels are high, it may be a one-to-many match, requiring further analysis or manual review. Through this multi-model fusion approach, the system can leverage the strengths of different models to improve matching accuracy and robustness.

[0087] In this embodiment, rule-based matching model construction involves building a series of judgment rules based on industry expert knowledge, including hard matching rules (such as exact phone number match) and soft matching rules (such as highly similar names and close geographic locations). Traditional machine learning model training involves using historical matching data to train multiple machine learning models (such as random forests, gradient boosting trees, and support vector machines) to learn the relationship between features and matching results. Deep learning matching model construction involves designing and training deep learning models (such as twin networks and attention mechanism networks) to capture the complex nonlinear relationships between hotel and room type features. Model prediction and scoring involves scoring each hotel / room type in the candidate matching set against the target object using rule-based, machine learning, and deep learning models to generate matching scores from multiple models. Model fusion and comprehensive scoring involves combining the predictions of multiple models into a final matching score matrix through model fusion techniques such as weighted averaging, stacking, or voting. Matching pattern recognition involves identifying different matching scenarios, such as one-to-one matching, one-to-many matching (one new hotel matches multiple hotels on the platform), and many-to-one matching, based on matching score patterns, and generating preliminary matching results.

[0088] In a preferred implementation of this embodiment, generating the preliminary matching result further includes topology optimization of a sparse evolutionary neural network, specifically including: Step S45: Based on the candidate matching set, a sparse MLP network structure is constructed, and a topological search space including the number of layers, the number of neurons, and the connection mode is defined to obtain an initial network topology.

[0089] In this step, the topology optimization technology of sparse evolutionary training is introduced to address the problems of parameter redundancy, waste of computing resources, and difficulty adapting to complex matching scenarios in traditional deep learning models. The system first constructs a sparse multilayer perceptron (Sparse MLP) network structure. Unlike traditional fully connected networks, sparse MLP allows for sparse connections in the network, meaning that not every neuron is connected to all neurons in the next layer. This sparse connection pattern can significantly reduce the number of parameters while maintaining the model's expressive power. A topological search space with multiple dimensions is defined: the number of layers, with possible values ​​ranging from 3 to 10 layers; the number of neurons per layer, with possible values ​​ranging from dozens to hundreds; and the connection pattern, including different modes such as full connection, local connection, and skip connection. During the initialization phase, various strategies may be employed: random initialization, which randomly samples a topology from the search space; domain knowledge-based initialization, which designs an initial topology tailored to the specifics 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 latter layers; and existing model-based initialization, which modifies successful existing network architectures (such as ResNet and DenseNet) to obtain an initial topology. This initial topology is often overparameterized, meaning it contains more layers and neurons than will ultimately be needed. This provides ample search space for subsequent evolutionary optimization. Constraints are also set, such as the minimum and maximum number of layers, the minimum and maximum number of neurons per layer, and the minimum connection density, to ensure that the resulting network structure is within a practically feasible range. In this way, the system obtains an initial network topology that serves as a starting point for subsequent evolutionary optimization.

[0090] Step S46: For the initial network topology, the optimal topology structure is searched in the search space by using a genetic algorithm, and evaluated by using a fitness function that comprehensively considers matching accuracy and model complexity to obtain an optimized network topology.

[0091] In this step, a genetic algorithm is used to perform evolutionary optimization on the initial network topology generated in step S45, searching for the most optimal network structure for the hotel matching task. A genetic algorithm is an optimization algorithm inspired by biological evolution, simulating mechanisms such as natural selection, crossover, and mutation to find the optimal solution in the search space. A fitness function is first defined to evaluate the quality of the network topology. This function comprehensively considers multiple objectives: matching accuracy, typically measured by the F1 score or AUC value on the validation set; model complexity, typically measured by the number of parameters, FLOPs (floating-point operations), or inference time; and sometimes factors such as model interpretability or performance on specific hardware are also considered. The fitness function is typically a weighted combination of these objectives, with the weights reflecting the relative importance of different objectives. Then, the evolutionary process begins: A population is initialized, containing multiple different network topologies, which may include the initial topology generated in step S45 and its variants. The fitness of each topology in the population is evaluated. Topologies with high fitness are selected as parents. Child topologies are generated through crossover operations, for example, by swapping certain layers or connection patterns between two parent topologies. Mutation operations are performed on these child topologies, randomly changing the number of neurons or connection patterns in certain layers. The fitness of the newly generated child topologies is evaluated. The population is updated, retaining topologies with high fitness and eliminating those with low fitness. This evolutionary process iterates over multiple rounds (typically dozens to hundreds of rounds) until a satisfactory topology is found or a preset number of iterations is reached. During the evolutionary process, the system may employ various techniques to improve efficiency, such as early stopping (stopping if no improvement has occurred after multiple rounds) and elite retention (ensuring that the best topologies are not lost during evolution). Ultimately, an optimized network topology is obtained, which strikes a good balance between matching accuracy and model complexity.

[0092] Step S47: Based on the optimized network topology, the interdependence between features is analyzed, and highly related features are grouped into the same module to form a modular grouping structure.

[0093] In this step, modular ordering technology is introduced. By analyzing the interdependencies between features, highly correlated features are grouped into the same module, forming a locally dense but globally sparse connectivity pattern. This modular grouping is a key mechanism for sparse MLP optimization, significantly reducing the number of parameters and computational complexity while maintaining the model's expressiveness. The system first performs a correlation analysis on the features in the hotel matching task, calculating mutual information (MI), Pearson correlation coefficients, or other correlation metrics. Based on these correlation metrics, the system constructs a feature dependency graph, where nodes represent features, edges represent dependencies between features, and edge weights indicate the strength of the dependencies. Community discovery algorithms (such as the Louvain algorithm and spectral clustering) are then applied to identify feature modules on the feature dependency graph, grouping highly correlated features into the same module. In the hotel matching scenario, these modules typically have clear semantic meanings. For example, the basic information module includes basic information such as the hotel name, address, and phone number; the location module includes geographic information such as latitude and longitude coordinates, administrative divisions, and surrounding landmarks; the facilities and services module includes the availability and ratings of various facilities and services; and the price review module includes price ranges, customer ratings, and review text. The system allocates different resources (such as the number of neurons) to different modules based on their semantic meaning and importance. Furthermore, the system analyzes inter-module dependencies and determines the connection pattern between them. For example, the basic information module and the location module may require more connections because they jointly determine the uniqueness of a hotel; whereas the facilities and services module and the price review module may require fewer direct connections. This modular grouping allows for the construction of a network structure that better suits the characteristics of the hotel matching task, ensuring sufficient interaction between key features while avoiding computational waste caused by unnecessary connections.

[0094] Step S48: For the modular grouping structure, a local dense and global sparse connection mode is used to perform network training to obtain a sparse evolutionary matching model.

[0095] In this step, based on the modular grouping structure formed in step S47, a neural network with locally dense but globally sparse connectivity is constructed and trained. This unique connectivity pattern is the core characteristic of the sparse evolutionary matching model. The system first designs the network architecture based on the modular grouping structure: dense connections are used within modules to ensure that features within the same module can fully interact and capture complex nonlinear relationships; sparse connections are used between modules, retaining only necessary cross-module connections to reduce the number of parameters and computational complexity. Skip connections or residual connections may also be introduced to enable direct information transfer between layers, alleviating the vanishing gradient problem and improving training efficiency. Training data is then prepared, including positive and negative examples from historical matches. Each example contains a pair of hotel feature vectors and matching labels. During training, a specially designed optimization strategy is employed: first, sparsity regularization, using L1 regularization or other sparsity-enhancing techniques to encourage the model to learn sparser weights; second, dynamic pruning, which periodically removes connections with near-zero weights to further improve the sparsity of the model; Furthermore, knowledge distillation techniques may be employed, using 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: matching accuracy and computational efficiency are assessed on a validation set. If performance does not meet requirements, the module partitioning or connection pattern may be adjusted and retrained. If the model is too sparse and lacks expressive power, the number of connections may be increased appropriately. If the model remains too complex, further pruning or quantization may be performed. Through this locally dense and globally sparse training strategy, the system ultimately achieves a sparse evolutionary matching model that maintains high matching accuracy while maintaining low computational complexity.

[0096] Step S49: using the sparse evolutionary matching model to score the candidate matching set, generating an optimized matching score matrix and the preliminary matching result.

[0097] In this step, the sparse evolutionary matching model trained in step S48 is applied to the actual hotel matching task, scoring the candidate matching set and generating an optimized matching score matrix and preliminary matching results. First, features are extracted for each hotel pair in the candidate matching set and organized according to a modular grouping structure. These features are then fed into the sparse evolutionary matching model, which calculates a matching score through forward propagation. This score, typically a value between 0 and 1, represents the probability that the two hotels are the same. Next, the matching scores for all candidate pairs are organized into a matching score matrix, where each element represents the matching score for a pair of candidate hotels. Based on this score matrix, matching pattern analysis is performed. If a newly added hotel's matching score with a hotel on the platform is significantly higher than that of other candidates (e.g., a score greater than 0.9, while the next highest score is less than 0.3), it is considered 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, requiring further analysis or manual review. If the scores for all candidate pairs are low (e.g., all less than 0.5), it may be that there is no corresponding hotel on the platform and it needs to be added as a new hotel. The matching status of each pair of hotels in different feature modules is also recorded, which helps to explain the matching results and provides a basis for subsequent manual review. For example, it may be found that two hotels have a high degree of matching in the basic information and location modules, but have large differences in the facilities and services modules. This may indicate that they are the same hotel, but the facility information of one supplier is not updated in a timely manner. By applying the sparse evolutionary matching model, more accurate matching results can be generated at a lower computing cost, especially when processing large-scale data, the efficiency improvement is particularly obvious. Actual 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 increase the inference speed by 3 times, which enables the platform to support the real-time hotel matching needs of more suppliers.

[0098] In this example, the core concept of sparse evolutionary training is to dynamically optimize the neural network's connectivity from the perspective of network topology using an evolutionary algorithm, rather than simply stacking more layers or using more neurons. In the hotel room matching scenario, the importance and relevance of different feature dimensions vary significantly, making traditional fully connected network structures prone to overfitting and computational inefficiency. The sparse MLP method, on the other hand, uses sparse connectivity and dynamic topological evolution to find the optimal network structure, retaining critical connections while pruning unimportant ones, achieving a "less is more" approach to structural optimization.

[0099] Specifically, the topology search space must first be defined, including the number of layers, the number of neurons per layer, and the connection pattern. Evolutionary algorithms (such as genetic algorithms and particle swarm optimization) are then used to search for the optimal topology within this search space. Evaluation criteria include matching accuracy, model complexity, and inference time. During the evolutionary process, topologies with good performance are retained and mutated, while those with poor performance are eliminated. After multiple rounds of iteration, a near-optimal network topology is achieved.

[0100] Modular Ordering (MO) is a key mechanism for sparse MLP optimization. It analyzes the interdependencies between features and groups highly correlated features into the same module, creating a locally dense but globally sparse connectivity pattern. In a hotel matching scenario, basic information such as the hotel name, address, and phone number might form one module, while facilities, services, and pricing might form another. Using sparse connections between modules and dense connections within modules ensures the model's expressive power while significantly reducing the number of parameters and computational complexity.

[0101] For example, when matching data for a new hotel named "Oriental Luxury Hotel," a traditional deep learning model might need to perform fully connected computations across all feature dimensions. However, a network optimized using sparse evolution automatically recognizes, based on historical matching experience, that the weighting of hotel names, location information, and star ratings should be increased, while the weighting of information like facility lists can be appropriately reduced. Through modular grouping and sparse connections, computing resources are concentrated on the most relevant feature combinations. In actual testing, this optimization has reduced model parameters by approximately 60%, increased inference speed by three times, and improved matching accuracy by 5-8 percentage points.

[0102] This technology is particularly well-suited for matching hotel information across suppliers, as the quality and completeness of data provided by different suppliers vary significantly. The sparse evolutionary network can adaptively adjust the network structure to suit the characteristics of different data sources, allowing for more flexible handling of complex matching scenarios. For example, for supplier data that lacks detailed addresses but has complete facility information, the network connections related to facility matching will be automatically strengthened. Conversely, for suppliers with detailed address information but lacking facility descriptions, the network structure will be adjusted accordingly to fully utilize the address information.

[0103] The key technical points that need to be noted during the implementation process include: (1) The design of the initial topology structure should be reasonably initialized based on domain knowledge; (2) The design of the fitness function of the evolutionary algorithm needs to comprehensively consider the matching accuracy and model complexity; (3) In order to prevent insufficient expression ability due to excessive sparsity, a minimum connection density constraint needs to be set; (4) The modular partitioning should be re-evaluated regularly to adapt to changes in data distribution.

[0104] By integrating the topology optimization technology of sparse evolutionary training 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 the real-time hotel matching needs of more suppliers at a lower computing cost.

[0105] In step S5, the final matching conclusion including matching status, matching object ID and matching confidence is generated through Bayesian uncertainty estimation and dynamic threshold adjustment, which specifically includes: Step S51: Based on the preliminary matching results, the distribution characteristics of the matching scores are analyzed, and the difference between the highest score and the second highest score is calculated to obtain the score distribution characteristics.

[0106] In this step, the preliminary matching results generated in step S4 are subjected to in-depth statistical analysis to assess the reliability and certainty of the matching results. First, the matching score distribution characteristics for each newly added hotel are extracted. These characteristics can reflect the certainty and potential ambiguity of the matching results. Key distribution characteristics include: the highest matching score (the score of the candidate hotel with the highest matching score), which reflects the absolute strength of the best match; the second-highest matching score (the score of the second-highest candidate hotel), which helps assess the relative strength of the best match; the gap between the highest and second-highest scores (the score interval), which is an important indicator of certainty. A larger interval indicates that the best match is significantly better than other candidates, making the matching result more reliable; the concentration of scores, which can be measured by calculating the variance or entropy of the scores of the top N candidate hotels. Low variance or entropy indicates that the scores are concentrated in a few candidates, suggesting that multiple reasonable matches may exist; and the decay rate of scores, which is the rate of decline from the highest score to subsequent scores, can be quantified by fitting an exponential decay curve. Rapid decay generally indicates a more certain match result. We also analyze the consistency of ratings for the same hotel pair across different matching models (rule-based, machine learning, and deep learning). Highly consistent ratings generally indicate a more reliable match. Furthermore, we compare the score distribution of the current matching task with the typical distribution patterns of historical matching tasks to identify unusual distribution patterns, which may indicate unique matching situations requiring additional attention. These detailed score distribution analyses enable a comprehensive assessment of the certainty and reliability of matching results, providing a solid statistical foundation for subsequent confidence estimates.

[0107] Step S52: applying the Bayesian method to the score distribution characteristics in combination with historical matching data to estimate the uncertainty of the matching result and generate a confidence index.

[0108] In this step, based on the score distribution characteristics analyzed in step S51, Bayesian statistical methods are applied, combined with historical matching data, to quantify the uncertainty of the matching results and generate a reliable confidence indicator. The core idea of ​​the Bayesian method is to treat matching judgment as a probabilistic inference problem. The posterior probability (the reliability of the matching result) is estimated by combining prior knowledge (historical matching experience) with current observations (matching score distribution). First, a Bayesian model is constructed: a random variable M is defined to represent whether two hotels are a match, and S represents the observed matching score distribution characteristics. Based on the historical matching data, the prior distribution P(M) is estimated, which is the probability that two random hotels are a match in the absence of any specific observations. Also based on the historical data, the likelihood function P(S|M) is estimated, which is the probability of observing a specific score distribution characteristic given the known matching of the two hotels. Then, Bayes' theorem is applied to calculate the posterior probability P(M|S) = P(S|M)P(M) / P(S). This is the probability that the two hotels are a match given the currently observed score distribution characteristics, and it is also a direct quantification of the confidence level. In practical implementation, various Bayesian methods may be employed: Naive Bayes, which assumes conditional independence of features and simplifies computation, but may ignore inter-feature correlations; Bayesian networks, which can model complex inter-feature dependencies but are more complex to construct and infer; and Monte Carlo methods, which approximate the posterior distribution through random sampling and are suitable for complex models. These methods estimate not only the probability of a match (a point estimate) but also the uncertainty of this probability (an interval estimate or the full posterior distribution), providing more comprehensive confidence information. For example, an estimate such as "the probability of a match between two hotels is 85% ± 5%" might indicate not only a high probability of a match but also low uncertainty. Furthermore, the contribution of different features to the confidence score is analyzed to identify the most critical features for the current match judgment. This helps explain the match results and guide subsequent manual review. Bayesian methods can transform match scores into confidence metrics with clear statistical interpretation, providing a reliable quantitative basis for business decisions.

[0109] Step S53: Based on the confidence index and in combination with business needs, the matching decision threshold is dynamically adjusted according to the accuracy and recall rate indicators to obtain an adjusted decision threshold.

[0110] In this step, the matching decision threshold is intelligently and dynamically adjusted based on the confidence index generated in step S52, combined with specific business needs and historical matching quality. The decision threshold is a key parameter for determining whether to accept a matching result and directly affects the matching accuracy and recall. First, clarify the business needs. Different business scenarios may have different preferences: some scenarios may place more emphasis on accuracy and would rather miss some matches than generate false matches, such as in hotel price comparison scenarios, where false matches may lead to incorrect price information; other scenarios may place more emphasis on recall and would rather generate some possible false matches than try to find all true matches, such as in resource integration scenarios, where the goal is to identify as many duplicate hotels on the platform as possible. Based on these business preferences, an initial decision threshold is set, usually a threshold that can achieve the desired balance between accuracy and recall based on historical data. Next, dynamic threshold adjustments are performed: Historical matching data is analyzed to model the relationship between confidence and matching accuracy, typically forming an S-shaped curve, with higher confidence indicating higher matching accuracy. Real-time matching quality is monitored. If accuracy declines, the decision threshold may be raised; otherwise, it may be lowered. Changes in data distribution are considered; if the distribution of new data differs significantly from historical data, the threshold may need to be recalibrated. Different thresholds may be set for different types of hotels or suppliers, as their matching difficulty and feature distribution may vary. A multi-threshold strategy is also implemented, with multiple decision thresholds set to categorize matching results into multiple confidence levels, such as high confidence (automatically accepted), medium confidence (requiring light review), and low confidence (requiring detailed review). Furthermore, the effectiveness of threshold adjustments is regularly evaluated, with metrics such as precision, recall, and F1 score calculated before and after the adjustments to ensure their effectiveness. This dynamic threshold adjustment mechanism adapts to varying business needs and data characteristics, achieving an optimal balance between precision and recall, and improving overall matching quality.

[0111] Step S54: Based on the matching score matrix and the confidence index, a decision matrix is ​​constructed to classify the preliminary matching results into four categories: confirmed match, possible match, confirmed mismatch, and manual review required.

[0112] In this step, a two-dimensional decision matrix is ​​constructed based on the match score matrix and confidence metrics. This matrix categorizes the preliminary matching results into different processing categories, enabling an intelligent decision-making process. The decision matrix typically uses the match score as the horizontal axis and the confidence as the vertical axis, dividing the two-dimensional space into multiple regions, each corresponding to a decision category. Match results are categorized into four categories: Definite matches (high match scores and high confidence indicate a high degree of confidence that the hotels are the same and can be automatically accepted without manual intervention); Possible matches (high match scores but medium confidence indicate a high probability of the hotels being the same, but with some uncertainty, potentially requiring light manual confirmation); Definite mismatches (low match scores and high confidence indicate a high degree of confidence that the hotels are not the same and can be automatically rejected without manual intervention); and Manual review required (requires manual review) refers to results with unusual combinations of match scores and confidence, such as high match scores with low confidence or medium match scores with significant discrepancies between models. These situations typically indicate complex or unique matching patterns and require detailed manual review. The boundaries of these four regions are dynamically determined based on the decision thresholds adjusted in step S53. Other factors are also considered to refine the decision matrix: the data quality and reliability of different suppliers; for lower-quality suppliers, the "requires manual review" region may be expanded; the historical matching success rate; for hotel types with historically high matching accuracy, the "certain match" region may be expanded; and the feature completeness; for hotels with incomplete features, the manual review ratio may be increased. The specific position of each match result in the decision matrix is ​​also recorded, along with the key features and model scores that led to the decision. This information helps explain matching decisions and supports subsequent manual review. By constructing such a decision matrix, human resources can be intelligently allocated, focusing manual review on complex or borderline cases that require the most human judgment, while automatically handling cases with high certainty, thereby improving overall matching efficiency.

[0113] Step S55: Based on the decision matrix and the adjusted decision threshold, determine the matching status and matching object ID, calculate the matching confidence, and generate the final matching conclusion.

[0114] 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 newly added hotel, including a clear matching status, matching ID, and a quantified matching confidence. First, the matching status of the newly added hotel is determined based on its position in the decision matrix: if it falls in the "Confirmed Match" area, the status is set to "Matched"; if it falls in the "Confirmed Unmatched" area, the status is set to "Unmatched"; if it falls in the "Possible Match" area, the status is set to "Possible Match"; if it falls in the "Requires Manual Review" area, the status is set to "Awaiting Review." For hotels with a "Matched" or "Possible Match" status, the matching ID is further determined—that is, the ID of the hotel that matches it on the platform. In most cases, this is the ID of the candidate hotel with the highest matching score. However, in some special cases, such as when multiple candidate hotels have similar and high scores, multiple possible matching IDs may be returned and marked as requiring further confirmation. Then, the matching confidence is calculated, a value ranging from 0 to 100% that quantifies the reliability of the matching result. The confidence level is typically based on the Bayesian posterior probability from step S52, but may be calibrated and adjusted to ensure comparability across different hotel types and supplier data. In addition to this basic information, a detailed description of the matching basis is generated, including: key matching features, such as name similarity and geographic distance, and their contribution to the matching result; the scores and weights of each model, explaining the impact of different models on the final result; a feature difference analysis, detailing the specific differences between the two hotels in various dimensions to help understand the basis for the matching judgment; and potential anomalies, such as outliers or inconsistencies in certain features, which may require special attention. Ultimately, all this information is integrated into a structured matching conclusion, which includes both machine-readable status and ID information and human-understandable explanations and rationale, providing comprehensive support for subsequent business processing and possible manual review. This approach not only informs users of "whether there is a match," but also "why that judgment was made" and "how reliable that judgment is," significantly improving the usability and credibility of matching results.

[0115] In this embodiment, match score distribution analysis refers to analyzing the distribution characteristics of match scores, including the gap between the highest and second-highest scores, and the concentration of scores, to provide a basis for confidence assessment. Bayesian uncertainty estimation applies Bayesian methods to estimate the uncertainty of match results based on historical match data and current matching patterns, generating a confidence metric. Dynamic adjustment of decision thresholds dynamically adjusts the match decision threshold based on business requirements (e.g., preferring a false match to a false match, or preferring a false match to a false match) and historical match quality. Decision matrix construction constructs a decision matrix based on match scores and confidence levels, categorizing match results into four categories: "definite match," "possible match," "definite mismatch," and "requires manual review." Match conclusion generation generates the final match conclusion based on the decision matrix, including the match status (whether it exists), the matching object ID (if so, which hotel / room type on the platform it corresponds to), and the match confidence (a quantified match indicator ranging from 0-100%). Match basis description generation generates a match basis description based on feature difference analysis and the contribution of each model to support business personnel in understanding and verifying the match results.

[0116] In a preferred implementation of this embodiment, generating the final matching conclusion further includes adaptive resource allocation based on a comparison ranking network, specifically including: Step S56: Based on the candidate matching set, a comparison ranking network is constructed, and the basic feature vectors of the hotel matching candidate pairs are input for comparison to obtain a prospect score.

[0117] In this step, an innovative comparative ranking network is introduced to intelligently allocate computing resources, addressing the resource challenges of large-scale hotel matching. The core of this step is to build a lightweight neural network that can quickly assess the "promises" of candidate matching pairs—that is, their likelihood of becoming a high-quality match after detailed evaluation. The comparative ranking network architecture is first designed. It typically adopts a Siamese network structure, consisting of two sub-networks with shared parameters, each processing the characteristics of a pair of hotels. Each sub-network can be a simple multi-layer perceptron or a lightweight convolutional network to ensure fast inference speed. The network input is a comparison of the basic feature vectors of a pair of hotels, including fast-to-compute features such as name similarity, location difference, and star rating difference. These features are inexpensive to compute but rich in information. The network output is a scalar value representing the "promises score" of the candidate pair. A higher score indicates a higher likelihood that the pair will be confirmed as a match after detailed evaluation. Training data is then prepared: positive examples (confirmed matching hotel pairs) and negative examples (confirmed mismatched hotel pairs) are collected from historical matching data. A basic feature vector comparison is computed for each hotel pair. The final matching result and confidence score after detailed evaluation are recorded for each hotel pair. During training, specially designed loss functions are employed: Contrastive Loss can be used to prioritize the prominence of true matches over false matches; Ranking Loss can be used to ensure that correctly matched candidate pairs are ranked higher than incorrectly matched pairs within the same query; and Triplet Loss can be used to consider the relationship between the query hotel, correct matches, and incorrect matches. To improve the model's generalization, various regularization techniques, such as Dropout and Batch Normalization, are employed, and cross-validation is used to tune hyperparameters. After training, model performance is evaluated on a validation set, with particular attention paid to ranking quality (e.g., mean average precision, NDCG, and other metrics) and computational efficiency. This contrastive ranking network allows for rapid identification of the most promising candidate matches before a full evaluation, providing crucial insight for subsequent resource allocation.

[0118] Step S57: performing stratification processing on the prospect scores, dividing the hotel matching candidate pairs into three levels: high, medium, and low according to the prospect scores, and obtaining a stratified candidate set.

[0119] In this step, based on the prospect scores calculated in step S56, candidate matching pairs are intelligently stratified to prepare for subsequent differentiated resource allocation. The purpose of stratification is to focus limited computing resources on those candidate pairs most likely to produce high-quality matches, while using lightweight processing for candidate pairs with lower likelihood, thereby significantly improving efficiency while maintaining matching quality. First, the distribution characteristics of the prospect scores are analyzed: the statistical characteristics of the scores, such as the mean, variance, and quantiles, are calculated; the distribution shape of the scores, such as whether there is a clear multimodal distribution, is observed; and the relationship between the prospect scores and the final matching results in the historical data is analyzed to determine the optimal stratification threshold. Then, stratification thresholds are set to classify candidate matches into three tiers: the high-prospect tier, which includes the candidate pairs with the highest prospect scores (e.g., the top 10% or those with scores above a certain threshold). These candidates are most likely to be correct matches and warrant the most computational resources. The medium-prospect tier, which includes candidate pairs with intermediate prospect scores (e.g., those with scores between two thresholds), has a certain probability of being correct matches, but with a higher degree of uncertainty, requiring moderate computational resources. The low-prospect tier, which includes candidate pairs with the lowest prospect scores (e.g., the bottom 50% or those with scores below a certain threshold), is likely not a correct match and requires only minimal verification. These thresholds are dynamically adjusted to adapt to the characteristics of different datasets and resource variations. Other factors are also considered when stratifying the candidates: query complexity. For complex queries (e.g., for hotels with incomplete or ambiguous information), the proportion of high-prospect tiers may be increased. The historical matching success rate. For hotel types that have historically been difficult to match, the stratification strategy may be adjusted. During peak load periods, the thresholds may be raised, reducing the number of candidate pairs in the high-prospect tier to conserve resources. Furthermore, the tier assignment information and its rationale for each candidate pair are recorded, which facilitates subsequent analysis and optimization of the tiering strategy. This tiered approach enables differentiated resource allocation, ensuring the quality of processing critical candidate pairs while avoiding wasting resources on low-value pairs, significantly improving overall matching efficiency.

[0120] Step S58: Based on the hierarchical candidate set, deep feature extraction and multi-model fusion scoring resources are allocated to hotel matching candidate pairs with high prospect scores, lightweight model evaluation resources are allocated to hotel matching candidate pairs with medium prospect scores, and basic rule judgment resources are allocated to hotel matching candidate pairs with low prospect scores to obtain hierarchical evaluation results.

[0121] In this step, based on the hierarchical candidate set from step S57, a differentiated resource allocation strategy is implemented, allocating different levels of computing resources and processing models to candidate matching pairs at different levels to optimize resource utilization. For high-prospect candidate pairs, the most abundant computing resources and the most complex models are allocated. Deep feature extraction is performed, including complex text semantic analysis, detailed geolocation comparison, and comprehensive facility and service comparison. These feature extraction processes may involve complex preprocessing and transformation. Multi-model fusion scoring is applied, including rule-based models, multiple machine learning models, and deep learning models, to comprehensively assess the likelihood of a match. Detailed feature difference analysis is performed to identify key matching points and potential inconsistencies. Comprehensive confidence metrics, including Bayesian posterior probabilities and uncertainty estimates, are calculated. For mid-prospect candidate pairs, moderate computing resources and lightweight models are allocated. Basic feature extraction is performed, processing only the most critical feature dimensions, such as name, address, and geolocation. Lightweight model evaluation, such as a simplified machine learning model or a small neural network, is applied to balance efficiency and accuracy. Only when the lightweight evaluation results indicate a possible high-quality match is a more detailed evaluation performed. For low-promising candidate pairs, only the most basic resources are allocated: a quick rule-based assessment is primarily applied, such as checking for hard mismatches (e.g., different cities, significant star rating differences). A more detailed assessment is only initiated when the rule-based assessment fails to confirm a match or when a potential match signal is detected. The effectiveness of resource allocation is dynamically monitored: the final matching results of candidate pairs at each level are tracked to evaluate the effectiveness of the tiering strategy; resource utilization is monitored to ensure optimal resource allocation; and resource allocation ratios are adjusted based on real-time feedback to optimize overall efficiency. This differentiated resource allocation strategy focuses limited computing resources on the most valuable candidate pairs, significantly improving matching efficiency. Practical applications have shown that this strategy can reduce computing resource requirements by over 70% while maintaining matching accuracy, particularly when processing large-scale multi-vendor data.

[0122] Step S59: performing matching quality monitoring on the hierarchical evaluation results. When the accuracy rate drops below a preset threshold, the weight and sampling ratio of the comparison ranking network are automatically adjusted to obtain an adaptive adjustment result.

[0123] In this step, a closed-loop adaptive optimization mechanism was established. By continuously monitoring the matching quality of tiered assessments, deviations in resource allocation strategies were promptly identified and corrected, ensuring high matching accuracy under diverse data conditions. First, comprehensive matching quality monitoring metrics were designed: matching accuracy at each tier, which is the proportion of correct matches in each tier; recall at each tier, which is the proportion of correct matches found in that tier relative to all correct matches; misclassification analysis, which focuses on cases with low prospect scores that are actually correct matches (false negatives) and cases with high prospect scores that are actually mismatches (false positives); and inter-tier quality variation, which compares the differences in matching quality across tiers to ensure appropriate stratification. These metrics are continuously collected and compared against historical baselines and expected targets, triggering automatic adjustments when anomalies are detected. The adjustment mechanism consists of two main aspects: first, weight adjustment of the comparative ranking network. This involves analyzing the characteristic patterns of misclassified cases to identify potential biases within the comparative ranking network. The network weights are then adjusted through online learning or incremental training to improve its ability to identify specific types of cases. Second, sampling ratio adjustment occurs. If a significant drop in accuracy is detected for a particular layer, the sampling ratio for that layer is increased. This involves randomly selecting more candidate pairs from that layer for detailed evaluation to collect more data and improve accuracy. For example, if multiple cases are detected that were originally low-scoring but are actually correct matches, the sampling ratio of the low-prospect layer is automatically increased, and the weights of the comparative ranking network are adjusted to make them more sensitive to such cases. Multiple adjustment trigger thresholds are set: a slight drop threshold triggers minor adjustments; a significant drop threshold triggers major adjustments and an alert; and an emergency threshold temporarily disables the tiered strategy and reverts to full evaluation. Furthermore, a gradual adjustment strategy is implemented, first validating the effects of adjustments on a small portion of the data before fully implementing them to avoid fluctuations caused by over-adjustment. This adaptive resampling mechanism allows for rapid strategy adjustments to maintain stable matching quality when data distribution changes or new matching patterns emerge. Since the comparative ranking network continues to learn from the online matching process, the resource allocation efficiency will continue to improve with the increase of running time, forming a virtuous circle.

[0124] Step S510: Based on the adaptive adjustment result and the hierarchical evaluation result, a resource optimized matching score matrix and the final matching conclusion are generated.

[0125] In this step, the hierarchical evaluation results from step S58 and the adaptive adjustment results from step S59 are integrated to generate the final matching score matrix and matching conclusion, completing the closed-loop of the entire adaptive resource allocation framework. First, the evaluation results at each level are merged: For high-prospect candidate pairs, the detailed scoring from multi-model fusion is directly applied. For mid-prospect candidate pairs, if the lightweight evaluation indicates a high-quality match, the processing may be upgraded and a detailed score obtained; otherwise, the lightweight evaluation result is used. For low-prospect candidate pairs, if the basic rule judgment is inconclusive or the candidate pair is selected by adaptive sampling, a more detailed score is obtained; otherwise, the basic rule judgment result is used. Next, a complete matching score matrix is ​​constructed, in which each element represents the matching score for a pair of candidate hotels. Unlike traditional methods, this matrix is ​​generated through resource optimization, and different elements may be based on different depths of evaluation. Next, the Bayesian uncertainty estimation and dynamic threshold adjustment methods from step S5 are applied to this optimized score matrix to obtain the final matching conclusion. When generating the final conclusion, special attention will be paid to processing the scores obtained through evaluations of different depths to ensure their comparability and consistency: the scores of different models may need to be calibrated so that the results of evaluations of different depths have consistent statistical interpretations; for those candidate pairs that have only undergone lightweight evaluations or basic rule judgments, their evaluation depth may be marked in the conclusion to indicate possible uncertainty; for those candidate pairs that have been re-evaluated during the adaptive adjustment process, the score changes before and after the adjustment will be recorded, which will help analyze the effect of the adjustment. Ultimately, a complete matching conclusion is generated that includes the matching status, matching object ID, and matching confidence. At the same time, resource usage statistics are also provided, such as the number of candidate pairs at each level, the number of calls to various models, the total computing resource consumption, etc. This information helps to evaluate and optimize resource allocation strategies. Through this resource optimization method, it is possible to maintain or even improve matching quality while significantly reducing computing costs, providing technical support for the platform to support larger-scale hotel resource integration. Practical applications have shown that this framework complements Bayesian uncertainty estimation well: the comparison ranking network focuses on optimizing the efficiency of resource allocation, while the Bayesian method focuses on quantifying the uncertainty of the final matching results. The combination of the two enables efficient processing of large-scale data while still providing reliable confidence assessment, providing strong support for business decision-making.

[0126] In this embodiment, the core of the resource adaptive allocation framework based on comparative ranking network is an online learning comparative ranking network, which can learn the relationship pattern between upper and lower layer solutions from the historical matching process, and then guide resource allocation decisions.

[0127] Specifically, a lightweight comparative ranking network is constructed. Its input is the basic feature vector comparison of hotel match candidate pairs (e.g., name similarity, location differences, star rating differences, and other fast-calculating features). The network output is a "prospect score" for each 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 using a relative ranking loss function, ensuring that true matches have higher prospect scores than non-matches.

[0128] In actual operation, all candidate matching pairs are first rapidly evaluated for their prominence, followed by tiered resource allocation based on the evaluation results. Specifically, candidate pairs are categorized into three tiers: high, medium, and low, based on their prominence scores, and computing resources of varying precision are allocated to each tier. For candidate pairs with high prominence scores, the most abundant computing resources are allocated, including deep feature extraction and multi-model fusion scoring. For candidate pairs with medium prominence scores, lightweight models are used for evaluation. For candidate pairs with low prominence scores, only basic rules are used for simple judgment.

[0129] Crucially, the framework introduces an adaptive resampling mechanism. During the matching process, the matching quality of candidate pairs at each level is continuously monitored. If the accuracy of a particular level decreases, the sampling rate and computing resource allocation for that level are automatically increased. For example, if multiple cases are detected that originally had low scores but are actually correct matches, the weights of the comparative ranking network are adjusted, and the sampling ratio of low-scoring candidate pairs is increased.

[0130] For example, when searching for a match for a newly added hotel named "West Lake Scenery Hotel" within a platform database of 100,000 hotels, traditional methods might require a comprehensive evaluation of thousands of candidate matching pairs. However, using a comparative ranking network can quickly identify approximately 100 highly promising candidate pairs (e.g., "West Lake Scenery Hotel" and "Hangzhou West Lake Scenery Hotel") and focus resources on in-depth evaluation of these pairs. The remaining pairs are then evaluated lightly or skipped, concentrating computing resources on the pairs most likely to produce high-quality matches.

[0131] Experiments show that this adaptive resource allocation framework can reduce computing resource requirements by over 70% while maintaining matching accuracy, particularly when processing large-scale multi-vendor data. Furthermore, because the comparative ranking network continuously learns from the online matching process, resource allocation efficiency improves with runtime.

[0132] This framework complements the Bayesian uncertainty estimation in step S5: while the ranking network focuses on optimizing resource allocation efficiency, the Bayesian approach focuses on quantifying the uncertainty of the final matching results. The combination of the two enables efficient processing of large-scale data while still providing reliable confidence assessments, providing strong support for business decision-making.

[0133] By introducing a resource adaptive allocation framework based on a comparative ranking network, hotel room matching can manage computing resources more intelligently, significantly reducing computing costs while maintaining or even improving matching quality, providing technical support for the platform to support larger-scale hotel resource integration.

[0134] In step S6, the parameters and thresholds of the matching model are optimized through online learning and regular batch training, specifically including: Step S61: Based on the final matching conclusion, key data and intermediate results in the matching process are persistently stored to establish a matching history database.

[0135] In this step, a comprehensive data persistence mechanism was designed and implemented to store the key data and intermediate results generated during the matching process, establish a structured matching history database, and provide a data foundation for subsequent model optimization and improvement. First, determine the scope of data that needs to be persisted, which typically includes: original input data, namely the original feature information of newly added hotels and candidate hotels, which is the basis for reproducing and verifying the matching process; feature processing results, including preprocessed standardized features and constructed multi-level feature vectors, which help analyze the effectiveness of feature engineering; the candidate matching set generation process, which records the screening logic and intermediate results, including inverted index hits, LSH algorithm hash codes, multi-dimensional similarity indicators, etc., which helps optimize the candidate generation strategy; scoring details of each model, including the specific scores of each pair of candidate hotels by the rule model, machine learning model, and deep learning model, as well as the weights and process of model fusion, which are crucial for analyzing model performance and improving fusion strategies; the confidence assessment process, which records the matching score distribution characteristics, the detailed calculation process of Bayesian uncertainty estimation, and the adjustment logic of the decision threshold, which helps optimize the confidence assessment mechanism; the final matching conclusion, including the matching status, matching object ID, matching confidence, and detailed matching basis description, which is the direct basis for evaluating matching quality. A multi-tiered storage strategy is employed: core results are stored in a relational database to support efficient query and analysis; large volumes of intermediate data may be stored in distributed files or object storage, balancing storage costs and access efficiency; critical processing logs are stored in dedicated logs to facilitate problem diagnosis and monitoring. A rational data lifecycle management strategy is also designed: the latest matching data is stored intact for immediate analysis and troubleshooting; historical data may be compressed or sampled for storage, balancing storage costs and data value; and particularly important matching cases (such as high-value hotels or cases with past errors) may be permanently preserved as benchmark data for model evaluation. This optimized data persistence mechanism enables the accumulation of rich historical matching data, providing a solid data foundation for subsequent model optimization and improvement.

[0136] Step S62: Collecting feedback information on the business's confirmation, correction and denial of the matching results from the matching history database to obtain business feedback data.

[0137] In this step, a comprehensive feedback collection mechanism was designed and implemented to proactively collect feedback and corrections from both business and user accounts, forming a closed-loop quality improvement system. First, multiple feedback collection interfaces were designed: a business API, allowing other business units (such as hotel management and reservations) to programmatically submit feedback, which is the primary source of feedback; an administrative 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 end-user reports of inconsistent or duplicate hotel information, which is a crucial supplement for identifying matching errors. Feedback typically includes several types: confirmation feedback, indicating that a matching result is correct. These positive examples help reinforce the model's correct judgment; correction feedback, indicating that a matching result is partially correct but requires adjustment, such as matching the correct hotel but with the wrong room type. This information helps refine the model's judgment criteria; negative feedback, indicating that a matching result is incorrect. These negative examples are particularly important for correcting model errors; and new matches, indicating unrecognized matching relationships. These missed cases help improve the model's recall. The collected feedback will be structured: the feedback format will be standardized to ensure that feedback from different sources can be handled uniformly; the original matching records will be linked to establish a complete link between the feedback and the matching process; the key points of the feedback will be extracted to identify the most critical information in the feedback, such as the type of error, correction suggestions, etc.; the reliability of the feedback will be evaluated and a credibility weight will be assigned to the feedback based on factors such as the source of the feedback and the historical accuracy of the feedback provider. In addition, feedback will be actively sought, especially for matching results with low confidence or large differences in scores between models. Review tasks may be automatically generated and business personnel may be requested for confirmation. A feedback incentive mechanism will also be implemented to encourage business personnel and users to provide high-quality feedback, such as through points, rankings, or other rewards. Through this comprehensive feedback collection mechanism, it is possible to continuously obtain evaluation and correction information on matching results, providing real and rich training data for subsequent model optimization.

[0138] Step S63: Based on the business feedback data, calculate the accuracy, recall and F1 score indicators, perform time series analysis, and obtain matching quality monitoring results.

[0139] In this step, a comprehensive matching quality assessment and monitoring system is established based on the business feedback data collected in step S62. Through multi-dimensional metric calculation and time series analysis, matching performance and changing trends are comprehensively evaluated. First, core matching quality metrics are calculated: Precision (the proportion of matches identified as matches that are actually matches, reflecting the accuracy of the judgment); Recall (the proportion of successful matches identified among all actual matches, reflecting the coverage); and F1 score (the harmonic mean of Precision and Recall), providing a comprehensive metric that balances the two. These metrics are calculated not only at an overall level but also across multiple sub-dimensions: Match quality across different hotel types (e.g., business hotels, resort hotels, etc.) to identify strengths and weaknesses in handling specific hotel types; Match quality across different regions (e.g., first-tier cities, second-tier cities, overseas destinations, etc.) to identify the impact of regional factors on matching; Match quality across different supplier data to assess adaptability to different data sources; Match quality across different confidence intervals to verify the accuracy of confidence estimates; and Individual performance of different matching models to assess their contributions and potential for improvement. These metrics are analyzed over time: trends are plotted over time to identify patterns of long-term improvement or degradation; month-over-month and year-over-year changes are calculated to assess the effectiveness of recent optimization measures; seasonality analysis is performed to identify periodic quality fluctuations, such as those observed during holidays; and anomaly detection algorithms are implemented to promptly identify sudden changes in metrics that may indicate changes in data distribution or glitches. Furthermore, error case analysis is performed: mismatch cases are clustered to identify common error patterns; the difference in feature distribution between correct and incorrect matches is calculated to identify potential biases; and the relationship between confidence and actual accuracy is analyzed to assess the calibration of confidence estimates. A detailed matching quality report is generated for technical and business teams, including a summary of key metrics, trend charts, anomaly alerts, and improvement suggestions. This standardized quality monitoring mechanism enables a comprehensive assessment of matching performance, identifies issues promptly, and provides clear guidance for subsequent optimization.

[0140] Step S64: Based on the matching quality monitoring result, an online learning method is used to fine-tune the parameters and decision threshold of the matching model in real time to obtain a real-time optimization result.

[0141] In this step, based on the matching quality monitoring results from step S63, an agile online learning mechanism is implemented. This mechanism responds to business feedback in real time and dynamically adjusts the matching model's parameters and decision thresholds, ensuring rapid adaptation to changes in data distribution and emerging matching patterns. First, an incremental learning strategy is designed: matching results with clear feedback, particularly those with incorrect judgments, are used as new training samples for incremental model updates. These samples are given higher weights because they represent weaknesses in the current model or recent data patterns. Next, a parameter fine-tuning mechanism is implemented: Feature weights are dynamically adjusted based on their ability to distinguish correct and incorrect matches. If a feature's discriminatory power decreases, its weight may be reduced. Model fusion weights are adjusted based on the performance of each model on recent data, with higher weights assigned to models with better performance. The 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 raised; if not, it may be lowered. Multiple online learning algorithms are employed: Online Gradient Descent (OGD), suitable for updating parameters in linear models; Online Random Forest (ORF), capable of incrementally updating decision tree ensemble models; and Online Bayesian Update (OBES), particularly well-suited for updating prior distributions in Bayesian uncertainty estimation. To ensure the stability of online learning, a series of protection mechanisms are implemented: parameter change limits to prevent drastic parameter changes caused by a single sample; performance monitoring to automatically roll back to the previous parameters if performance degrades after an online update; and sample diversity checks to ensure that the samples used for updates are sufficiently representative, preventing the model from favoring specific data types. A parameter version history is maintained, recording the parameter values ​​of each update, the samples that triggered the update, and the performance changes after the update. This facilitates analysis of online learning results and troubleshooting. This real-time fine-tuning mechanism enables continuous optimization of model performance without service interruption, rapidly adapting to changes in data distribution, and improving the robustness and adaptability of matching.

[0142] Step S65: Based on the real-time optimization results and business feedback data, the matching model is periodically retrained in batches, the parameters of the matching model are updated, and the matching model with optimized parameters is generated.

[0143] In this step, based on the real-time optimization results from step S64 and the accumulated business feedback data, a standardized batch retraining mechanism is implemented to regularly and comprehensively update the matching model, ensuring that the model can fully absorb historical experience and adapt to long-term changes in the data distribution. First, the trigger conditions for retraining are determined: time trigger, such as regular weekly or monthly full model retraining; data volume trigger, when a sufficient amount of new feedback data has accumulated (for example, when the new feedback data reaches 10% of the original training set); performance trigger, when the performance indicators that cannot be effectively improved by online learning continue to decline, more comprehensive retraining is initiated; data distribution change trigger, when a significant change in the input data distribution is detected (such as the addition of a large number of new types of hotels), retraining is initiated to adapt to the new distribution. Then, prepare the retraining dataset: merge historical training data and newly collected feedback data to form a more comprehensive training set; clean and balance the data to ensure that different types of samples have a suitable proportion to avoid the model being biased towards a specific category; pay special attention to difficult samples that perform poorly on the current model, and they may be oversampled or given higher weights; considering the timeliness of the data, older data may be given lower weights, and more emphasis may be placed on the latest matching patterns. Next, perform comprehensive model retraining: for rule matching models, the parameters of the rules may be adjusted or rules may be added or deleted based on the new data; for machine learning models, the model will be retrained using an updated training set, and new feature combinations or model structures may be tried; for deep learning models, training will continue (fine-tuning) based on the current model, or in some cases, a new model will be trained from scratch; for model fusion strategies, the performance of each model will be re-evaluated and the fusion weights will be adjusted. After retraining is complete, a comprehensive model evaluation is conducted: the new model's performance is evaluated on a retained validation set to ensure improved performance. Particular attention is paid to scenarios that previously underperformed to verify that the new model has addressed these issues. A / B testing is conducted, comparing the new model with the current online model on real traffic to ensure that the results meet expectations. Finally, the new model is deployed cautiously: a trial run is first conducted on a small portion of traffic to monitor performance and stability. Once confirmed to be problem-free, coverage is gradually expanded, ultimately replacing the old model entirely. A snapshot of the old model is retained to enable a quick rollback if issues arise with the new model. Detailed information about each retraining is recorded, including training data characteristics, model parameter changes, and performance improvements. This helps track the model's evolution and accumulated experience over time. This regular batch retraining mechanism enables more comprehensive and in-depth model optimization based on real-time fine-tuning, ensuring that matching continues to improve performance and adapt to long-term changes in business needs and data characteristics.

[0144] In this embodiment, matching result persistence refers to the persistent storage of matching conclusions, key data from the matching process, and intermediate results, establishing a matching history database. Business feedback data collection refers to the design and implementation of a feedback mechanism to collect feedback from businesses regarding matching results, such as confirmation, correction, or denial. Matching quality monitoring refers to the continuous monitoring of 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 the use of online learning methods to fine-tune model parameters, particularly feature weights and decision thresholds, in real time for matching results with clear feedback. Regular batch retraining refers to the regular batch retraining of the matching model after collecting sufficient feedback data, updating model parameters to adapt to changes in data distribution. Algorithm iteration and optimization refers to the continuous optimization of the matching algorithm based on matching quality analysis and error case studies, introducing new features or models, and improving overall matching performance.

[0145] See also Figure 3 , Figure 3 This is a structural block diagram of the hotel room type matching device provided by an embodiment of the present invention. Figure 3 As shown, the hotel room type matching device provided in this embodiment includes: 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 data set; A feature construction module 302 is configured to construct a basic feature vector, a semantic feature vector, and a geographic spatial feature vector based on the standardized hotel and room type feature dataset through feature engineering to obtain a multi-level feature vector; A candidate generation module 303 is configured to generate a candidate matching set by filtering the entire hotel and room type data on the platform based on the multi-level feature vectors in combination with an inverted index and a locality sensitive hashing algorithm; A matching scoring module 304 is configured to construct a matching model based on the candidate matching set using 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; A decision module 305 is configured to generate a final matching conclusion including a matching status, a matching object ID, and a matching confidence level based on the preliminary matching result and the matching score matrix through Bayesian uncertainty estimation and dynamic threshold adjustment; The optimization module 306 is used to optimize the parameters and thresholds of the matching model through online learning and regular batch training based on the final matching conclusion and collected business feedback data to obtain a matching model with optimized parameters.

[0146] The specific implementation of each of the above modules corresponds to the aforementioned method embodiment and will not be repeated here.

[0147] The hotel room type matching method and device provided by the embodiments of the present invention achieves efficient and accurate cross-supplier hotel and room type matching through six steps: multidimensional feature data collection 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 method addresses the technical issues of insufficient accuracy, confidence assessment, and self-optimization capabilities in prior art hotel room type cross-supplier matching. It improves the ability of hotel reservation platforms to integrate resources from multiple suppliers, providing users with a wider range of choices.

[0148] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A hotel room type matching method, characterized in that: include: Obtaining original information of newly added hotels and room types, preprocessing the original information, and generating a standardized hotel and room type feature dataset; Based on the standardized hotel and room type feature dataset, constructing basic feature vectors, semantic feature vectors, and geospatial feature vectors through feature engineering to obtain a multi-level feature vector; Based on the multi-level feature vector, the inverted index and locality sensitive hashing algorithm are combined to filter the hotel and room type data from the platform to generate a candidate matching set; 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 ​​calculated and integrated to generate a preliminary matching result; Based on the preliminary matching results and the matching score matrix, generating a final matching conclusion including matching status, matching object ID and matching confidence through Bayesian uncertainty estimation and threshold dynamic 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 regular batch training to obtain a matching model with optimized parameters.

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

3. The method according to claim 1, characterized in that The basic feature vector, semantic feature vector and geographic space feature vector are constructed by feature engineering to obtain a multi-level feature vector, including: Based on the standardized hotel and room feature dataset, extract hotel name, hotel star, hotel address, room name, room area, and bed type features to construct a basic feature vector; Converting text information in the standardized hotel and room type feature dataset using a pre-trained language model to generate a semantic feature vector; Based on the geographic information in the standardized hotel and room type feature dataset, constructing a geospatial feature vector including latitude and longitude coordinates and administrative division codes; The basic feature vector, the semantic feature vector and the geographic space feature vector are subjected to weight fusion and dimensionality reduction processing to generate the multi-level feature vector.

4. The method according to claim 1, wherein The inverted index and locality sensitive hashing algorithm are combined to filter the platform's full hotel and room type data to generate a candidate matching set, including: Based on the multi-level feature vector, an inverted index based on hotel name and hotel address is constructed, and a subset of candidate hotels is obtained by quickly filtering the hotel by city, administrative division, and star rating. Applying a locality sensitive hashing algorithm to the multi-level feature vector to map it into a low-dimensional hash code, and searching for a set of potential similar objects; Calculating name similarity, geographical location similarity, and facility similarity for the set of potentially similar objects to obtain a multi-dimensional similarity index; Sorting and truncation are performed based on the weighted scores of the multi-dimensional similarity indicators to generate the candidate matching set.

5. The method according to claim 1, wherein The matching model is constructed by using a rule matching model, a machine learning model, and a deep learning model, and then a matching score matrix is ​​calculated and integrated to generate preliminary matching results, including: Based on the candidate matching set, scoring is performed using hard matching rules and soft matching rules to obtain a rule matching score; Scoring the candidate matching set using random forest, gradient boosting tree, and support vector machine models to obtain a machine learning matching score; Scoring the candidate matching set using the Siamese network and the 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 averaged and fused to generate the matching score matrix and the preliminary matching result.

6. The method according to claim 1, wherein The Bayesian uncertainty estimation and threshold dynamic adjustment are used to generate the final matching conclusion including the matching status, matching object ID and matching confidence, including: Based on the preliminary matching results, analyzing the distribution characteristics of the matching scores, calculating the difference between the highest score and the second highest score, and obtaining the score distribution characteristics; Applying the Bayesian method to the score distribution characteristics in combination with historical matching data to estimate the uncertainty of the matching results and generate a confidence index; Based on the confidence index and in combination with business needs, the matching decision threshold is dynamically adjusted according to the accuracy and recall rate indicators to obtain an 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: confirmed match, possible match, confirmed mismatch, and manual review required; 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.

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 transformation, including: Performing a discrete Fourier transform on the standardized hotel and room type feature dataset to convert the multidimensional data from the time domain to the frequency domain to obtain 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; Performing spectrum energy distribution analysis 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 geographic space feature vector to generate an enhanced multi-level feature vector.

8. The method according to claim 5, characterized in that Generating the preliminary matching result also includes topology optimization of a sparse evolutionary neural network, including: Based on the candidate matching set, a sparse MLP network structure is constructed, a topological search space including the number of layers, the number of neurons and the connection pattern is defined, and an initial network topology is obtained; For the initial network topology, an optimal topology structure is searched in a search space by using a genetic algorithm, and a fitness function that comprehensively considers matching accuracy and model complexity is used for evaluation to obtain an optimized network topology; Based on the optimized network topology, the interdependence between features is analyzed, and highly related features are grouped into the same module to form a modular grouping structure; For the modular grouping structure, a local dense and global sparse connection mode is used to perform network training to obtain a sparse evolutionary matching model; The candidate matching set is scored using the sparse evolutionary matching model to generate an optimized matching score matrix and the preliminary matching result.

9. The method according to claim 6, characterized in that Generating the final matching conclusion also includes adaptive resource allocation based on the comparison ranking network, including: Based on the candidate matching set, a comparative ranking network is constructed, and basic feature vectors of hotel matching candidate pairs are input for comparison to obtain a prospect score; Performing stratification processing on the prospect scores, dividing the hotel matching candidate pairs 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 prospect scores, lightweight model evaluation resources are allocated to hotel matching candidate pairs with medium prospect scores, and basic rule judgment resources are allocated to hotel matching candidate pairs with low prospect scores, thereby obtaining a hierarchical evaluation result; Performing matching quality monitoring on the hierarchical evaluation results, and automatically adjusting the weights and sampling ratios of the comparison ranking network when the accuracy drops beyond a preset threshold, to obtain an adaptive adjustment result; Based on the adaptive adjustment result and the hierarchical evaluation result, a resource-optimized matching score matrix and the final matching conclusion are generated.

10. The method according to claim 1, characterized in that Optimizing the parameters and thresholds of the matching model through online learning and periodic batch training includes: Based on the final matching conclusion, key data and intermediate results in the matching process are persistently stored to establish a matching history database; Collecting the business system's feedback information on the matching results, including confirmation, correction, and denial, from the matching history database to obtain business feedback data; Based on the business feedback data, calculate the accuracy, recall and F1 score indicators, perform time series analysis, and obtain matching quality monitoring results; Based on the matching quality monitoring results, an online learning method is used to fine-tune the parameters and decision thresholds of the matching model in real time to obtain a real-time optimization result; Based on the real-time optimization results and business feedback data, the matching model is periodically retrained in batches, the parameters of the matching model are updated, and the matching model with optimized parameters is generated.

11. A device for matching hotel room types, characterized in that: include: A 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 data set; A feature construction module is used to construct a basic feature vector, a semantic feature vector, and a geospatial feature vector through feature engineering based on the standardized hotel and room type feature dataset to obtain a multi-level feature vector; A candidate generation module is used to filter the hotel and room type data on the platform based on the multi-level feature vector, combined with an inverted index and a locality-sensitive hashing algorithm, to generate a candidate matching set; A matching scoring module is used 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; A decision module, configured to generate a final matching conclusion including a matching status, a matching object ID, and a matching confidence level based on the preliminary matching result 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 through online learning and regular batch training based on the final matching conclusion and collected business feedback data to obtain a matching model with optimized parameters.

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