204results about How to "Accurate recommendation" patented technology

The invention discloses a simulation model auxiliary system for a cigarette cut tobacco manufacturing workshop, an operation method, electronic equipment and a storage medium. Comprising a workshop simulation model mark management module, a workshop simulation basic knowledge extraction module, a workshop simulation multi-mode knowledge graph construction module, a workshop simulation knowledge interaction and problem diagnosis module and a workshop system quality evaluation module. According to the invention, the workshop simulation model and the multi-modal knowledge graph are associated through the simulation model marking and management module, and model recommendation of a dialogue system is supported; a basic knowledge extraction module is utilized to extract data from MES, ERP, PLM and process documents, a multi-modal knowledge graph is constructed, and the construction efficiency is improved; the knowledge interaction module analyzes user requirements and provides structured support through knowledge graph retrieval; the optimization suggestion and problem diagnosis unit is combined with various reasoning methods to analyze requirements and provide diagnosis and optimization suggestions; and the system quality evaluation module ensures the reasoning accuracy, integrity and practicability, and provides guarantee for simulation model optimization and decision making.

The invention relates to the technical field of e-commerce, in particular to an integral welfare platform commodity recommendation method based on a large language model, and the method comprises the following steps: 1, collecting data; step 2; step 3, model training; 4, performing personalized recommendation; the device comprises a data collection module, a data preprocessing module, a model training module, a recommendation algorithm module and a recommendation generation module. The commodity recommendation method aims at performing more accurate and personalized commodity recommendation on the user by introducing a large language model and a mixed recommendation algorithm and combining behavior data, basic information data and credit data of the user on a point welfare platform and other platforms, so that the user experience and the platform service quality are improved. According to the method, accurate analysis and commodity recommendation of user point use habits are realized by utilizing collaborative filtering, content-based recommendation and a mixed recommendation algorithm, and meanwhile, the intelligence and adaptability of a recommendation system are enhanced by combining multi-platform credit data.

The invention discloses an intelligent customer service training method and device of a multi-modal large model and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: collecting multi-modal data; performing data duplicate checking on the multi-modal data; preprocessing the duplicate checking data; performing data enhancement on the preprocessed data; the method comprises the following steps: constructing an initialized multi-modal large model based on a Transform architecture; training the initialized multi-modal large model according to the enhanced data, and obtaining a target multi-modal large model; acquiring user inquiry information; extracting weight characters in the user inquiry information; inputting the weight characters into the target multi-modal large model to obtain associated information; preprocessing the associated information to generate a feature vector; obtaining a similarity value between the feature vector and the weight character according to a similarity algorithm; ranking the similarity values according to a sequence to obtain a ranking result; extracting a sorting result; determining target associated information based on the target similarity value; and displaying the target associated information.

The invention belongs to the field of intelligent environment comprehensive regulation and control, and particularly discloses and provides an intelligent environment comprehensive regulation and control method based on sleep aiding, which comprises the following steps: firstly, according to the heart rate and skin resistance of a user, evaluating the stability of a sleep state, automatically adjusting the brightness and color temperature of light, and creating a suitable sleep atmosphere; secondly, determining the temperature and humidity of the preference environment by analyzing the longest sleep time, the sleep time and the number of times of waking up at night in the historical sleep data of the user, and intelligently regulating and controlling the environment to match the temperature and humidity of the preference environment of the user. And finally, the system recommends a suitable music type according to the environmental noise type, and accurately recommends sleep music based on preference feedback of the user for each piece of music. Meanwhile, environmental noise decibel is monitored in real time, music volume is dynamically adjusted, harmonious coexistence of music and environmental noise is ensured, a more comfortable and quiet sleep environment is created for the user, and the sleep quality of the user is improved in multiple dimensions.

The invention discloses a voice interaction sales agent based on a large language model and a sales vehicle. The voice interaction sales agent is constructed through the following steps: step 1, collecting multi-modal data; step 2, constructing a retrieval enhancement generation framework; 3, finely adjusting the large language model; 4, designing an intelligent voice interaction strategy; 5, multi-modal data processing and enhancement are carried out; step 6, designing a functional module; and step 7, system integration and deployment: the voice interaction sales agent is in butt joint with the vehicle-mounted hardware equipment so as to acquire multi-modal data, and all the function modules in the step 6 are integrated. According to the voice interaction sales agent, the intelligent level, the sales efficiency and the user experience of the self-service vending vehicle are effectively improved.

The invention relates to a knowledge base content recommendation method and system based on large model and subdivision field matching. The method comprises the steps that key content feature words are automatically extracted from original content currently read by a user based on a feature word weighted extraction algorithm; forming cue words based on the feature words and the names of the subdivided fields, and determining professional subdivided fields according to the cue words; aiming at each determined subdivision field, searching contents related to the feature words in a database or a knowledge base by adopting a semantic similarity retrieval algorithm, and extracting first M most related results; on the basis of the feature words and the search results of the subdivision fields, prompt words are formed, the first L most relevant results are calculated from the M search results to serve as recommendation content, and corresponding recommendation reasons are generated; summarizing the recommended contents determined in each subdivided field, and storing the summarized recommended contents in a database; and calling the recommended content of each subdivided field from the database. According to the method, automatic and low-cost accurate recommendation can be realized, and a large amount of manual data annotation is not needed.

The invention discloses a recommendation method combining a large language model and a knowledge graph. The recommendation method comprises the following steps: processing a large language model text based on an iterative aggregation thought; carrying out text coding based on BERT; aligning entity embedding based on the graph attention network; and recommending and predicting. By effectively integrating the large language model, the knowledge graph and the graph neural network technology, the performance and accuracy of the recommendation system are remarkably improved.

The invention relates to the technical field of virtual reality, in particular to an old people social interaction platform based on virtual reality, which comprises a user management unit, a virtual scene creation unit, a real-time interaction unit, a multi-dimensional social ecological adaptation unit and a situation-driven social game unit, the user management unit is used for user registration, personal information management and social account maintenance; the virtual scene creating unit allows a user to customize a virtual scene according to requirements; the real-time interaction unit provides voice, video and text chat functions; the multi-dimensional social ecological adaptation unit constructs a social ecological graph of the user; the context-driven social game unit designs a series of social games based on the current context of the user. According to the invention, the flexibility and applicability of the platform are enhanced, so that the platform is more immersive and attractive, and meanwhile, interaction and communication among users are increased through the storage and sharing functions of the scene.

The invention discloses an intelligent hospital guide method and system based on symptom matching and department recommendation, and the method comprises the following steps: receiving a multi-modal symptom description inputted by a user, and carrying out the combined processing, thereby obtaining multi-modal symptom information; inputting the multi-modal symptom information into a multi-modal symptom deep learning model to obtain text key symptom features and voice key symptom features, and performing feature fusion to obtain comprehensive symptom feature representation; performing multi-dimensional matching according to the comprehensive symptom feature representation to generate a candidate department list; acquiring the real-time position of the user, inputting the real-time position of the user, the candidate department list and the voice key symptom features into a department recommendation model together, outputting a recommended department by calling a # imgabs0 # minimum spanning tree path algorithm, and generating a department information graph for the recommended department; and displaying department information, a doctor-seeing path and an appointment mode for the user through the department information atlas, thereby realizing intelligent hospital guide. According to the invention, the symptom information of the user is accurately identified and efficient matching with related departments is realized.

The invention relates to the technical field of employment management, in particular to a flexible employment management system based on a block chain and big data and a skill evaluation method. According to the invention, through autonomic skill label selection of the user and intelligent skill index generation, recommendation and matching are more accurately carried out on service supply and demand parties; according to the technical scheme, skill authentication information of a user is linked through the block chain technology, the skill authentication information comprises skill certificates and training experiences, the key problems of information asymmetry, non-transparent evaluation, low matching efficiency, insufficient right and interest guarantee and the like in a traditional system are effectively solved through combination of the block chain technology and the big data technology, and the user experience is improved. According to the flexible employment management system, a complete and coherent closed loop is formed from skill authentication to matching recommendation to right and interest guarantee and occupational development, the efficiency and fairness of a flexible employment market are greatly improved, and the sense of trust and the degree of participation of users are enhanced.

The invention discloses an information flow advertisement matching system and method based on user browsing and consuming behaviors, and relates to the technical field of advertisement matching, and the matching method comprises the following steps: obtaining any user behavior record, and carrying out the effectiveness evaluation of the user behavior record; carrying out feature extraction by adopting a corresponding feature extraction strategy based on the effectiveness evaluation result of any user behavior record; performing influence degree analysis on each extracted feature, and establishing a behavior feature evaluation model to perform feature evaluation on the user behavior record; adjusting the behavior characteristic evaluation model according to the difference condition presented by the two evaluation results to obtain a characteristic threshold for judging the validity of each user behavior record; feature information extraction is carried out on advertisement information presented by any advertisement, and the matching effectiveness of the advertisement is evaluated; and judging whether to recommend based on an evaluation result, and carrying out priority ranking on the recommended advertisements.

The invention relates to a combined commodity recommendation method and device, equipment and a medium, and the method comprises the steps: determining an association rule index between all commodities in a historical behavior commodity data set of a user, and constructing a directed weighted graph based on the association rule index; determining a starting commodity node in the directed weighted graph; based on the starting commodity node, executing a random walk operation on the directed weighted graph to obtain a plurality of commodity paths, and vectorizing the commodity nodes in the commodity paths to obtain a node vector of each commodity node; fusing the text vector, the picture vector and the node vector corresponding to each commodity in the commodity path to obtain a comprehensive vector of each commodity; and calculating the similarity between the commodity corresponding to the starting commodity node and other commodities in the commodity path based on the comprehensive vector, and screening out the combined commodity corresponding to the starting commodity node according to the similarity. According to the method and the device, more accurate combined commodity recommendation is realized by fusing multi-dimensional features.

The invention discloses a one-stop human resource outsourcing intelligent service platform system, which comprises a talent pool management module, an interview management module, a project management module, an attendance management module, a salary management module, an intelligent risk management module and a notification and announcement module, the project management module is used for follow-up and management of projects; the attendance management module is used for making and managing attendance strategies; the salary management module is used for calculating and managing personnel salary; the intelligent risk management module is used for intelligent risk assessment, early warning and analysis; according to the one-stop human resource outsourcing intelligent service platform system provided by the invention, a talent retrieval recommendation algorithm based on naive Bayes is proposed to retrieve and intelligently recommend information in a talent pool; and an enterprise risk assessment algorithm based on artificial intelligence is provided to carry out intelligent assessment, analysis and prediction on the enterprise risk.

The invention discloses a news content intelligent recommendation method based on a big language model and comparative learning. The method comprises the following steps: constructing a fine-tuning training data set, and performing fine-tuning training on the big language model; constructing a training set, inputting the training set into the fine-tuned large language model to obtain confidence prediction of graph structure enhancement, and cleaning to obtain a graph structure enhancement view; clustering information is extracted; the method comprises the following steps of: obtaining a collaborative embedding vector of a user and news contents through combined training of a graph neural network and graph contrast learning, obtaining a final embedding vector for recommendation through a multi-layer perceptron after the collaborative embedding vector is combined with a clustering embedding vector, and calculating an interaction score of the user and different news contents according to the final embedding vector. And recommending the news content to the user according to the interaction score. According to the method, the uncertainty in graph structure enhancement can be reduced, and key information is reserved as far as possible for joint training, so that a better recommendation effect is obtained.

The invention provides a personalized recommendation method and system for hot events, and relates to the technical field of big data analysis, and the method comprises the steps: collecting multi-source real-time data; preprocessing the multi-source real-time data to obtain multi-source real-time preprocessed data; performing weighted fusion on the multi-source real-time preprocessing data to obtain a weighted fusion score; generating a hotspot event based on the weighted fusion score; and retrieving the hot events to generate personalized recommendation contents meeting user requirements. According to the invention, real-time and intelligent generation of the hotspot event is effectively realized, personalized hotspot event recommendation is provided for the user, and the user experience is improved.

The invention discloses an intelligent patent retrieval method and system based on a large language model reordering technology, objects processed by the method comprise a variety of input data including patent public numbers, free texts and text files, key information is extracted, query texts are constructed, retrieval recall is executed, sparse and dense vector retrieval technologies are combined, and the retrieval efficiency is improved. And results are fused through a min sum merge algorithm. The de-noising step uses a sliding window strategy and vector coding to extract information fragments related to the query text. And finally, by training a reordering model and finely adjusting a recommendation list, the ranking of retrieval results is optimized, and the accuracy and correlation of retrieval are improved. According to the intelligent retrieval method, the patent retrieval efficiency and accuracy are improved.

The invention is applicable to the field of product recommendation, and discloses a sensor-based product recommendation method, which comprises the following steps of: collecting a product use data set of a user through a sensor, extracting a plurality of feature vectors, and aligning time sequence data to form user product use behavior features under a unified time scale. Then, the similarity between the users is calculated, a similarity matrix is constructed, strong correlation users are screened through a set threshold value, and a user similarity relation graph is formed; and based on the relation graph, identifying user communities with similar interests and behavior patterns, predicting potential relation strength among the users, and constructing a social network for cooperative use of products. And meanwhile, user portraits are enriched in combination with user behavior data in related fields. And finally, based on the constructed social network and the user portrait, comprehensively considering the user similarity and the product similarity, generating a candidate recommendation list, and adjusting weights of different similarities according to historical behaviors of the user, thereby obtaining a personalized product recommendation result.

A next interest point recommendation method fusing global periodicity and local regularity comprises the steps that 1, historical data are obtained and preprocessed, and a data set is obtained; 2, extracting related attributes in the data set to form a sign-in sequence of the user; 3, constructing a graph structure for a graph convolutional network by using the distance span information in the short-term sign-in sequence of the user, and updating the embedded representation of the interest point; 4, coding the interest point sign-in sequence of the user to generate a sign-in embedded representation, and dividing the sign-in embedded representation into sign-in sequence embedded representations taking sign-in as a unit and taking day as a unit; 5, according to the embedded representation of the sign-in sequence, constructing a short-term preference embedded representation and a long-term preference embedded representation representing the user; 6, predicting the probability that the next interest point of the user is a certain place according to the long-term and short-term preference embedding representation of the user; and 7, selecting several optimal interest points for recommendation to obtain a recommendation set. By means of the method, more personalized and more accurate recommendation of the next interest point is achieved.

The invention relates to the technical field of intelligent teaching, and particularly provides an interpretable exercise recommendation method and system based on relation attributes, and the method specifically comprises the following steps: processing the interaction data between a user and an exercise in an existing data set, building a user-exercise knowledge graph according to the relation between the data, and carrying out the initialization; inputting the initialized user-exercise knowledge graph instances into a TransE model for training to obtain embedded vectors of trained entities and relationships; and performing user exercise recommendation on the user-exercise knowledge graph, formalizing the user exercise recommendation into a Markov decision process, and performing multi-step path reasoning on the user-exercise knowledge graph by constructing a reinforcement learning system to realize user exercise recommendation. According to the method, a more accurate user portrait is realized, the accuracy of the recommendation system is improved, and meanwhile, the path between the recommendation question and the question practiced by the student is extracted from the knowledge graph to generate interpretable recommendation.

The invention relates to the field of information matching, in particular to a building material intelligent purchase matching and recommendation method, which comprises the following steps: S1, building material product feature acquisition; s2, analyzing user demand characteristics; s3, performing similarity analysis; s4, user evaluation analysis; s5, carrying out regional applicability analysis; s6, building material product recommendation analysis; according to the method, the similarity between the characteristics of the various building material products and the user demand characteristics is obtained through analysis according to the characteristic parameters of the various building material products and the user demand characteristic evaluation parameters, and the user evaluation coefficients of the suppliers of the various building material products are obtained through obtaining the user evaluation analysis of the suppliers of the various building material products. According to the method, regional climate applicability and regional environment applicability of various building material products are obtained, regional applicability evaluation coefficients of the various building material products are obtained through analysis, recommendation indexes of the various building material products are obtained through comprehensive analysis, and all preferred building material products are screened out in combination with prices; and the building material product meeting the actual demand of the user can be more accurately recommended to the user.

The invention relates to a commodity recommendation method and device, equipment and medium in the technical field of e-commerce, and the method comprises the steps: responding to a commodity recommendation event of an independent station shop, and determining a target recommendation scene and a target user corresponding to the event, obtaining candidate commodity sets recalled by a plurality of recall sources corresponding to the scene and an initial recall score of each candidate commodity in the candidate commodity sets, and correspondingly adjusting the initial recall score of each candidate commodity in each candidate commodity set to determine a target recall score according to the corresponding effect weight of each recall source compared with other recall sources; when a dynamically generated historical recommendation effect result of an independent station shop represents that user behavior evaluation needs to be carried out on the shop, determining a user behavior score of a target user associated with each recalled candidate commodity, and determining a recommendation score based on a target recall score of each candidate commodity and the user behavior score; and a plurality of recommended commodities are selected and pushed to the target user. According to the invention, the personalized recommendation of the shop is realized.

The invention relates to an article recommendation method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a training data set which is document data or text data containing the interaction information between each sample user and each sample article; aiming at a pre-established recommendation model, constructing a recommendation loss function based on a preset Rayleigh divergence coefficient and a learnable user personalized threshold parameter; inputting the training data set into a recommendation model, and training the recommendation model based on a recommendation loss function to obtain a completely trained recommendation model; the output of the recommendation model is the predicted score of the document about the specific article or the predicted score of the text about the specific article; and inputting historical interaction data between the target user and the target article into the recommendation model to obtain predicted scores about the target article, and carrying out article recommendation based on the sorting of the predicted scores. Through the method and the device, the problem that the articles cannot be accurately recommended to the user is solved, and the articles can be accurately recommended to the user.

The invention discloses an intelligent marketing scene construction method and system based on user behavior track big data, and relates to the technical field of big data. The method comprises the following steps: collecting data of a user behavior track, and preprocessing the data; identifying the data information, and classifying and summarizing the behavior track data information; acquiring data distribution and rules according to data information of the data information base; constructing a user portrait according to the data distribution and rule; constructing an intelligent marketing scene model according to the user portrait, and recommending a predicted marketing scene to the user by the predicted marketing scene recommendation; performing real-time monitoring and analysis according to the received real-time data information, returning data to the intelligent marketing scene model, and optimizing and adjusting the intelligent marketing scene model; according to the method, the behavior track data information of the user can be obtained through big data, so that enough data support is provided for the user before shopping, and commodities meeting the requirements of the user are recommended for the user according to different requirements of the user.

The invention relates to the technical field of semantic understanding, in particular to an automobile product matching system and method based on a large language model. The method comprises the following steps: receiving selling point information of a target vehicle type input by a vehicle enterprise, and constructing target selling point features according to semantics of the selling point information; obtaining a plurality of recommendation interest circle layers output by the large language model based on target selling point features; according to matching records of a plurality of historical interest circle layers and selling point features, obtaining distances of different interest circle layers; constructing a circle layer association graph of each interest circle layer according to the distance; mapping the plurality of recommended interest circle layers into a circle layer association graph, and selecting extended interest circle layers within a set range around the plurality of recommended interest circle layers; and pushing to a user terminal of the interest circle layer. According to the invention, the appropriate crowd can be matched for the automobile product and accurately pushed to the terminal of the crowd, so that the system operation efficiency is improved, and the effect of accurate recommendation is achieved while network resources are saved.

The invention provides a user preference analysis method and device oriented to right and interest products, belongs to the field of intelligent recommendation algorithms, and is used for solving the problem that the right and interest product ordering rate is low in the related art. The method comprises the steps that behavior feature data of a target user is obtained, the behavior feature data carries a timestamp and a right and interest product identifier, and the timestamp and the right and interest product identifier are sent to the target user; the behavior characteristic data comprises a characteristic value of each behavior characteristic dimension; substituting the behavior characteristic data of the target user into a self-researched preference analysis model to obtain preference degree data of the target user for each right and interest product; according to the invention, the preference degree data of the target user for each right product can be intelligently and reasonably analyzed based on the behavior characteristic data of the target user, and the quantitative preference of the target user for the right product can be accurately analyzed with low cost and high efficiency, so that the ordering rate of the right product can be improved with low cost and high efficiency.

The invention provides an intelligent product recommendation method based on customer credit, and the method comprises the steps: obtaining a customer credit score and guarantee valuation feature information, and judging the credit granting qualification and the initial credit granting limit of a customer according to a preset customer credit threshold value and a guarantee value threshold value; client credit increasing mode information is obtained, including adding mortgage and providing guarantee, a risk slow release score is obtained according to a guarantee estimated value after credit increasing and a client credit score, and a credit line is adjusted for the second time; and presenting an optimized product recommendation list to the customer, the list including product details, risk prompts and purchase channels, and recording feedback and purchase behaviors of the customer on recommended products to continuously improve the performance of the intelligent recommendation system.

The invention provides an expert recommendation method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving project text information of a target project and a screening condition of the target project sent by a user side, and carrying out the feature extraction of the project text information, the extracted item feature vectors are matched with expert feature vectors of all experts in a pre-created expert vector database, so that an initial expert recommendation list is generated according to a matching result, and credibility information of all the experts in the initial expert recommendation list is inquired; sorting experts in the initial expert recommendation list based on the creditworthiness information, screening the sorted experts in the expert recommendation list according to the screening condition of the target project, obtaining a final expert recommendation list, and sending the final expert recommendation list to the user side; according to the method, quick and accurate expert recommendation can be realized, the user satisfaction is effectively improved, the requirements of different users are better met, and the personalized degree of recommendation is enhanced.

The invention relates to a personalized enterprise knowledge recommendation system and method for a smart office platform, and belongs to the technical field of data mining and knowledge recommendation. Comprising a user behavior perception type multi-source information embedded representation learning module which is used for calculating a personalized business portrait representation vector based on business platform data, user role data and user personalized data of a current user; the document attribute perception type enterprise document representation learning module is used for calculating a comprehensive document embedding representation vector of each knowledge document based on the enterprise knowledge documents and the document attributes; the knowledge recommendation module is used for recommending an enterprise knowledge document to the user based on the set similarity of the personalized business portrait representation vector and each comprehensive document embedded representation vector; and the training module is used for training and adjusting related parameters based on the recommended enterprise knowledge document. According to the system and the method, accurate and reasonable documents can be provided for users according to specific service information in an enterprise office platform.

The invention discloses a chat tool-based group and position recommendation method and device, equipment and a medium. The method comprises the following steps: creating a plurality of group categories; setting the group attribute of each initial group for a plurality of initial groups created in the chat tool; the group attribute comprises a group attribute name, a target group category to which the group attribute belongs, a matching priority and a matching screening item; according to the matching priority, matching the portrait data of the target user with the group attribute of each initial group in sequence; in the matching process, if the ith initial group is successfully matched and the target user does not join the ith initial group, classifying the ith initial group or a subgroup thereof into a recommendation list; if the target user joins the ith initial group, continuing to match the next matching priority; recommending the group in the recommendation list to the target user; and pushing the target position to the target initial group and / or subgroup successfully matched with the target position. The group recommendation efficiency, the recruitment efficiency and the success rate can be improved.

The invention relates to the technical field of internet medical treatment, and provides a doctor recommendation method based on inquiry service quality and fairness and a medium. The method provided by the invention mainly comprises the following steps: S1, according to a disease problem uploaded by each patient, personal basic attribute information data of each doctor, and historical answer and comment data of each doctor, obtaining attribute features, interaction features and comment features, and further obtaining a doctor inquiry service quality score by using a factorization machine; s2, constructing a fair value function, obtaining a system exposure level and a target exposure level of a doctor according to the position model, and further obtaining a fairness constraint of a doctor end; and S3, constructing a doctor recommendation model, obtaining a final doctor recommendation set by using a multi-arm machine method based on improved Thompson sampling, and recommending the doctor recommendation set to the patient. According to the invention, by considering the inquiry service quality and the fairness constraint, the doctor recommendation accuracy is ensured, and the fairness of the recommendation system is effectively improved.