312 results about "Recommendation service" patented technology

The invention discloses an intention classification method and device based on vector retrieval and context awareness and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of extracting business metadata and associating the business metadata with typical problem examples to generate a standardized service description document; encoding the standardized service description document into a high-dimensional semantic vector through a pre-training language model, and constructing a neighbor search index to store the high-dimensional semantic vector; splicing the user identity information and the current question text into an enhanced query statement, and encoding the enhanced query statement into a context-aware dynamic query vector through a semantic model; performing similarity retrieval based on the dynamic query vector to obtain candidate intelligent services, performing business domain filtering, context weighted sorting and dynamic priority rearrangement, and outputting target recommendation services; by collecting interactive behavior data of a target recommendation service, quality scoring is performed on service descriptions and problem examples based on a preset evaluation rule, and the service descriptions and the problem examples of which the quality scores are lower than a quality threshold value are updated.

The invention provides a cross-domain multi-dimensional query feature index selection method, which comprises the following steps of: judging a query field and a query purpose of a user through query subject classification and query intention identification according to historical query behavior data of the user, and obtaining a field tag and an intention tag of the query; a navigation path of knowledge links is optimized, semantic correlation between nodes and small world index algorithm characteristics are comprehensively considered, an optimal knowledge navigation path is recommended for a user, and an optimization strategy is dynamically adjusted according to query historical behaviors and feedback of the user; a knowledge navigation result is presented in a visual interaction mode, a multi-dimensional attribute screening and sorting function is provided, exploration type browsing and deep mining of a user are supported, and personalized knowledge recommendation service is provided according to a query scene and preference of the user.

The invention relates to the technical field of new user data recommendation, in particular to an e-commerce personalized recommendation data system fused with deep learning, which comprises a data acquisition and processing unit, a dynamic monitoring unit, a GAN data enhancement unit and a recommendation service unit. The multi-modal data of users and commodities is comprehensively collected, features are accurately extracted, the dynamic monitoring unit monitors and predicts data changes in real time and generates adjustment signals by applying quantum state space and causal inference network technologies, and the GAN data enhancement unit generates high-quality simulation data based on a deep convolutional generative adversarial network by means of meta-learning, knowledge distillation and other algorithms. And the recommendation service unit is used for training a model according to the enhanced data, so that accurate recommendation is realized, the exposure degree of new commodities is improved, and the accuracy and efficiency of recommendation are improved.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention discloses a personalized recommendation method driven by user intention recognition, which comprises the following steps of: collecting multi-dimensional information such as browsing records, click behaviors and comment data of a user, and constructing a user behavior data set; preprocessing the collected user behavior data to obtain a session sequence; the current demand of the user is speculated by analyzing the input text and the behavior mode of the user, and the intention of the user is recognized; constructing a user interest preference prediction model according to the identified user intention; and in combination with real-time feedback of the user, the recommendation model is dynamically updated, and the response speed and the individuation degree of the recommendation system are improved. The problem that a traditional recommendation system only depends on static data and does not comprehensively consider real-time feedback of users is solved, self-adaptive ability is injected for personalized recommendation, intention behaviors of different users in multiple scenes can be analyzed and understood, accurate recommendation service is provided, and recommendation accuracy and user satisfaction are improved.

The invention belongs to the technical field of medical treatment and public health, and particularly relates to a hypertension group knowledge recommendation method and system based on a large language model multi-agent. Hypertension knowledge question-answer pair information, medical short video information and user personal health portraits are finely extracted by constructing a hypertension knowledge question-answer pair library; according to the method, video content quality evaluation and matching degree evaluation are performed, and user platform interaction data adjustment is combined, so that personalized and accurate knowledge recommendation services can be provided for hypertension users, the requirements of hypertension patients for health knowledge are met, the accuracy and effectiveness of knowledge recommendation are improved, and self-health management of the patients is facilitated.

A system for providing context-specific item recommendations is provided, for example in support of customer loyalty programs. In examples, a contextual offer recommendation engine utilizes a deep neural network in an epsilon-greedy agent to implement a contextual multi-arm bandit. The contextual multi-arm bandit is used to explore optimal solutions regarding correspondence between offers and customers. The optimal solutions may represent customer-offer combinations which may be published to a campaign manager for display to a customer, e.g., via a retail server. The deep neural network may be continually and adaptively retrained an extended based on observed actions between customers and new or preexisting offers.

The invention relates to the technical field of knowledge management systems, and discloses a knowledge full-life-cycle management system and a construction method, and the system comprises a technical platform and multi-modal perception layer, a calculation and arrangement engine layer, a unified API gateway layer, a multi-modal processing module layer, a knowledge graph layer, a cognitive reasoning layer and an application layer. Real-time collection and batch processing of multi-source heterogeneous data are achieved through a standardized SDK / API; spark / Flink is combined with Kubernetes to complete the ETL (Extract Transform Load) and resource scheduling of the multi-modal data; constructing an entity-relationship-attribute knowledge graph through cross-modal feature fusion; intelligent decision support is realized based on rule reasoning, graph calculation and GNN; the application layer provides intelligent questioning and answering, decision support and personalized recommendation services; the technical problems of knowledge islands, sharing barriers, knowledge statics and the like in the prior art are solved.

The invention discloses a personalized cross-domain recommendation method and system based on federal learning, and the method provides a personalized cross-domain recommendation service for a user on the premise that original data of interaction between the user and an article and user parameters are kept locally. The method comprises two stages of federated training: stage 1, intra-domain users cooperatively train a single-domain score prediction model by using a neural collaborative filtering method; in the second stage, overlapping users of the two domains cooperatively train a migration module based on a multi-layer neural network to capture a mapping relation represented by potential user features between the two domains; besides, each layer of network of the cross-domain recommendation model is decomposed into a base vector and a personalized vector which respectively represent common knowledge among different users and unique knowledge of the users, and a local model obtained by final training can provide personalized recommendation services for registered users in a target domain, so that the recommendation efficiency is improved, and the user experience is improved. Meanwhile, the global model obtained through training provides effective initial recommendation for new users in the target domain, and the cold start problem in a recommendation algorithm is effectively relieved.

The invention discloses an automatic construction system for a biological information analysis process, and the system comprises an intention understanding and semantic analysis module which is used for analyzing a natural language text inputted by a user into a structured task description meeting the requirements of a biological information analysis task; the knowledge graph and retrieval module is responsible for constructing a knowledge graph special for the bioinformatics field so as to provide knowledge retrieval and recommendation services; the process generation core module is used for receiving the structured task description, actively associating the knowledge graph with the retrieval module so as to supplement the field large model, and generating a biological information analysis target process language code; and the execution and monitoring module is used for guaranteeing workflow execution, full-life-cycle state monitoring, real-time fault diagnosis and intelligent self-healing decision making of target process language codes. According to the system, the executable analysis process can be directly generated according to the natural language requirement of the user, the tool compatibility and parameter validity are verified through the borrowed knowledge graph before execution, the dependency on the programming ability of the user is greatly reduced, and the process operation reliability is improved.

The invention provides a book recommendation method and system based on multi-source data and a user portrait, and the method comprises the steps: obtaining multi-source user data, and carrying out the cleaning and preprocessing of the multi-source user data, and obtaining effective data; in combination with a clustering analysis algorithm and an association rule mining algorithm, analyzing the effective data, and constructing to obtain a user portrait; generating a book recommendation list matched with the user portrait based on the user portrait in combination with a collaborative filtering algorithm and a content recommendation algorithm; the book recommendation list is pushed to a user terminal, a feedback result is obtained, and the feedback result is used for adjusting algorithm parameters and user portraits. The diversity and accuracy of recommendation results are improved, personalized book recommendation services can be provided based on user portraits, the utilization rate of library resources is remarkably improved, and the reading experience of users is improved.

The invention provides a site selection method and device of a service site, electronic equipment and a computer readable storage medium, and relates to the technical field of computers. The site selection method of the service site comprises the following steps: acquiring address information of all orders in a target area and business sites to which the orders belong; performing clustering processing on all the orders based on the address information and the business points to obtain a plurality of clustering arrays; obtaining associated data of each clustering array, and determining a site selection demand point of the service site from the clustering centers corresponding to the plurality of clustering arrays according to the associated data; the associated data comprises at least one of the number of waybills, the transportation distance, the waybill collection result and the site selection cost. The site selection of the service site can be accurately recommended, and convenience is provided for effective construction of the service site.

According to the intelligent recommendation method and device based on driving behavior analysis provided by the embodiment of the invention, accurate analysis of user interests is realized by innovatively constructing a multi-dimensional feature fusion mechanism and integrating registration information, video data, driving data and scene perception data. And designing a trajectory prediction model based on a recurrent neural network, and establishing a feature weighted fusion strategy for intelligent matching in combination with a user interest modeling network of an attention mechanism. And an online learning mechanism is introduced, and model parameters are continuously optimized through clicking, staying duration and visit record data, so that dynamic adjustment of personalized recommendation contents is realized. According to the method, the defects of the traditional technology in the aspects of feature extraction, interest modeling, recommendation strategies and the like are effectively overcome, and the recommendation service level and the user experience in the driving scene are remarkably improved.

The invention discloses application of privacy matrix decomposition on medical recommendation service based on a large language model. The system comprises a data acquisition module; a privacy matrix decomposition module; a large language model processing module; a recommendation decision module; the user interface module breaks through the restrictions of regions, time and professional resources through personalized online medical recommendation services, can obtain high-quality medical consultation and health recommendation services for social vulnerable groups such as remote areas, rural areas, old people and patients with mobility difficulties, promotes fair distribution of medical resources, enhances user trust, and improves the user experience. Particularly, due to the application of a privacy matrix decomposition technology, worries of a user on personal health data safety are eliminated, the trust degree of the user on a medical platform is improved, health consultation service popularization is promoted, the overall health management consciousness and the self-health-care ability of the society are improved, medical knowledge and health management concepts are popularized by means of a convenient online platform, and the health management system has a wide application prospect. Misdiagnosis and treatment caused by asymmetry of medical information are reduced, and the health quality of the whole society is improved.

The invention discloses a service recommendation method and device, electronic equipment and a computer program product. The method comprises the steps that archive information and scene information of a to-be-recommended user are collected, the archive information is used for representing the personal condition of the to-be-recommended user, and the scene information is used for representing the environment condition of the to-be-recommended user and user behaviors fed back by the to-be-recommended user; analyzing the archive information by using a preset user portrait model to obtain a to-be-recommended user portrait of the to-be-recommended user; fusing the to-be-recommended user portrait of the to-be-recommended user with the scene information to obtain a fusion feature of the to-be-recommended user; and recommendation services conforming to the fusion features are selected from the multiple preset services, each preset service has a corresponding feature tag, and the recommendation services are the preset services with the feature tags conforming to the fusion features. According to the method and the device, the technical problem that accurate recommendation cannot be carried out aiming at the service required by the user is solved.

In order to solve the problems of low resource utilization efficiency and insufficient personalized service in the prior art, the invention discloses an itinerary planning method, system, equipment and medium based on a guided local search algorithm, and relates to the technical field of big data analysis and artificial intelligence, and the method comprises the steps: obtaining and preprocessing scenic spot data and traffic data; filling a playing list based on the scenic spots selected by the tourists, the scenic spot data and the traffic data; and planning a travel for the travel list based on a guided local search algorithm. Multi-source data including scenic spot opening and closing time, recommended travel duration, geographic position, scenic spot labels and the like are integrated, real-time analysis and intelligent decision are performed by applying an advanced artificial intelligence technology, high-quality travel recommendation service is provided for tourists, personalized requirements of the tourists can be met, the utilization efficiency of tourism resources can be improved, and the tourism experience of the tourists can be improved. Efficient sharing and intelligent analysis of tourism information are realized, and powerful support is provided for tourism decision making.

The invention provides a communication method and a related device, which can be used in the technical field of communication. In the technical scheme provided by the invention, a first network element can send a first message to a first network data analysis function NWDAF network element, the first message is used for requesting the first network data analysis function network element to recommend service quality parameters, and the first message comprises at least one group of service quality parameters. In the method, a first NWDAF network element may determine a first QoS parameter based on a first message, the first QoS parameter may include one or more groups of the at least one group of QoS parameters, and the first QoS parameter may meet requirements of a consumer. The condition that the service requirement cannot be met or the service cannot be executed due to the fact that the QoS parameter recommended by the first NWDAF network element does not meet the requirement of the first network element is avoided.

The invention discloses a smart retail scene consumer behavior analysis method and system based on big data, and the method comprises the steps: collecting consumer behavior data from a retail platform, and carrying out the preprocessing; and carrying out behavior feature extraction and modeling according to the preprocessed data. And constructing an optimized causal reasoning driven behavior attribution model, and providing personalized behavior prediction and recommendation according to a model prediction result. According to the method, multi-dimensional analysis and modeling of consumer behaviors are realized, and deep connection among the consumer behaviors is revealed through a causal reasoning technology. The innovation step can break through the limitation of the prior art and provide more accurate and personalized behavior prediction and recommendation services. Specifically, the system can accurately identify and predict future demands of consumers according to behavior data collected in real time, thereby helping retailers to formulate more targeted marketing strategies, and improving consumer satisfaction and enterprise income.

The invention discloses an enterprise different-stage data matching service method based on AI artificial intelligence. The method comprises the following steps: S1, collecting operation data of an enterprise; s2, preprocessing the operation data; s3, extracting a time characteristic index sequence, and constructing an enterprise development stage evolution path; s4, constructing an enterprise development stage identification model, and generating an enterprise development stage label and a matching trend vector; s5, taking the enterprise development stage label and the matching trend vector as query conditions, and retrieving candidate service resource items; and S6, calculating a basic matching score by using the service resource matching model, performing directional adjustment, and outputting a recommended service resource list. According to the method, AI modeling and a comparative learning mechanism are fused, enterprise stage identification and trend-driven recommendation are realized, and the method has the advantages of high intelligence, accurate matching and high adaptability.

The invention relates to the technical field of terminals, and discloses a service recommendation method and electronic equipment. According to the method, based on information included in a current display interface of the electronic equipment, when the electronic equipment detects operation on first information included in the current display interface, the first information can serve as reference information, and service recommendation can be carried out on a user. Then, when the electronic equipment determines that the recommended service exists, the user can be prompted to check through the shortcut entry, for example, through a flickering prompt strip and the like. Therefore, the user can conveniently and directly view the recommendation service through the shortcut entry, and the user does not need to manually open other applications or other display interfaces based on the first information, so that the operation steps of the user can be saved, the operation efficiency can be improved, and the use convenience and experience of the user can be improved.

The embodiment of the invention provides a split-screen interaction method and electronic equipment, the method is applied to the electronic equipment, the method comprises the steps that a first split-screen interface and a second split-screen interface are displayed, the first split-screen interface displays the content of a first application, the second split-screen interface displays the content of a second application, and the first application is different from the second application; in response to operation of a user in the interaction area of the first split-screen interface, the electronic equipment displays second content associated with first content displayed in the interaction area on the second split-screen interface, and / or the electronic equipment displays recommendation service associated with the first content displayed in the interaction area on the second split-screen interface. Through the method or the electronic equipment, the interactive content selected by a user in one split screen can be perceived by another split screen, and the interactive content can be responded in the other split screen, so that the efficiency of processing multiple tasks by the user in a split screen manner can be improved, and the split screen use experience of the user is improved.

The invention relates to the field of big data analysis, in particular to an electric vehicle charging station recommendation method and device based on driver charging preference. According to the method, personal information and historical charging behavior data of a current user are collected, and a mileage anxiety threshold L of the user is updated. Collecting state information of a vehicle driven by a current user and operation data of a charging station; when a user puts forward a charging demand, candidate charging stations are screened firstly; calculating the payment cost Cb, the parking time cost Ct, the mileage anxiety cost Ch and the charging utility Ui of the user arriving at any candidate charging station i according to the collected information and the L of the current user; calculating the selection probability Pi of each candidate charging station according to the Ui; and the Ct and the Ch are ranked in a low-to-high order to generate a recommendation list. The problem that the existing charging station recommendation service cannot meet the personalized preference of the user is solved.

The invention discloses a lightweight cross-domain recommendation method and system based on user alignment Agent driving. The method comprises the following steps: firstly, acquiring historical behavior data of a user in multiple fields, fusing multi-modal contents such as texts and images, generating a fine-grained interest prototype through a cross-domain semantic encoder, and constructing a personalized Agent to simulate the intention of the user; then, in a multi-field collaborative environment, an Agent behavior strategy is optimized by utilizing reinforcement learning and a mixed reward mechanism, general preference and field specific preference are modeled through a hierarchical strategy network, and knowledge fusion is realized through a gating mechanism; and then, in combination with a preference distillation technology, extracting transferable characterization from Agent behaviors, and constructing a lightweight cross-domain knowledge graph. Finally, behavior track compression and cross-domain preference mapping are adopted, and efficient and low-consumption personalized recommendation is achieved. According to the method, the problems of cross-domain data sparsity and model complexity are effectively relieved, recommendation accuracy and system response efficiency are improved, and the method is suitable for real-time recommendation service of multiple scenes.

This disclosure discloses a content recommendation method and apparatus. The content recommendation method includes: receiving, from a terminal device, content request information for requesting to acquire a recommendation content item corresponding to a target user; determining whether a recommendation server is faulty; acquiring a first recommendation content item according to a recommendation cache corresponding to the target user in response to the recommendation server being faulty, wherein a plurality of recommendation content items are stored in the recommendation cache corresponding to the target user, and the plurality of recommendation content items are written when the recommendation server is not faulty; and sending the first recommendation content item to the terminal device.

The invention discloses an intelligent recommendation system for health management, which belongs to the technical field of intelligent medical treatment and comprises an acquisition module, a knowledge analysis module, a calculation processing module and an interaction module. The system collects original health data, manages a health knowledge graph and a safety recommendation rule base, constructs an initial health portrait by using the original health data, generates a first recommendation list and a second recommendation list based on the initial health portrait and a logic rule base, generates a recommendation card through the first recommendation list and the second recommendation list, and sends the recommendation card to a user. And collecting user behavior data for feedback. According to the method, the problems of high cold start difficulty, insufficient safety guarantee and limited personalized effect in the existing health recommendation service are solved, the initial availability and recommendation scientificity of the system are improved, the health behavior of the user can be effectively guided, and the method has positive significance in improving the health level of people.

Systems and methods for improving efficiency and control compliance across software development life cycles using domain-specific controls are disclosed. In one embodiment, a method may include: (1) receiving, by an analysis and recommendation service computer program, an identification of a domain for code; (2) identifying, by the analysis and recommendation service computer program, rules and / or control patterns for the domain; (3) receiving, by the analysis and recommendation service computer program, the code; (4) checking, by the analysis and recommendation service computer program, the code for compliance with the rules or control patterns; and (5) deploying, by analysis and recommendation service computer program, the code in response to the code being in compliance.

The invention provides a disease consumable recommendation method and system based on multi-agent cooperation, electronic equipment and a medium, and relates to the technical field of medical consumable recommendation. According to the technology of the invention, the related data of disease consumables are obtained based on the data sensing agent, and feature extraction is carried out on the related data of the disease consumables; constructing a consumable knowledge base based on the consumable related data; based on the extracted features, matching the AI decision agent in a consumable knowledge base, and outputting a plurality of intelligent recommendation results; the multiple intelligent recommendation results are fused, a medical consumable recommendation scheme is generated, and the service execution agent executes related services based on the medical consumable recommendation scheme. The disease type consumable recommendation technology can realize full-process intelligent management from disease type identification to consumable recommendation, has strong learning ability, reasoning ability and decision-making ability, and can provide accurate and personalized consumable recommendation services for different disease types.

Provided is a method for providing a user interface pattern recommendation service. In the method: (a) an administrator terminal registers app information; (b) a user terminal selects the app information and inputs a question to request a chatbot query; (c) a platform server refers to a platform DB and transmits key data including an UI pattern, a user preference, and a selection history via an information inquiry API; (d) an AI model within the platform server receives user input data, analyzes the data in real time using RAG technology and a self-trained model, and recommends a UI pattern preferred by a user; (e) a chatbot response module within a chatbot server receives the chatbot query; (f) the chatbot response module generates a chatbot response by referring to a chatbot DB; and (g) the chatbot response API receives the generated chatbot response and transmits the response to the user terminal.

The invention relates to a time sequence enhanced and multi-granularity intention guided adversarial generation recommendation method, which comprises the following steps of: firstly, obtaining an original user commodity interaction sequence and interaction time data, and obtaining a time sequence enhanced sequence through a plurality of data enhancement modes; then, by constructing a plurality of cross entropy loss functions and comparison loss functions, model training is constrained from different angles, and user behavior patterns and potential correlation are fully mined; and finally, performing joint training on the feature coding module, and performing fine adjustment on the prediction module to obtain a recommendation model. The method focuses on solving the problems of data sparseness and insufficient model generalization ability of sequence recommendation (SR) under information overload. Under the background that information technology development causes serious information overload, although the SR is concerned, the SR faces many challenges; a traditional method depends on project prediction task optimization parameters, is easily influenced by data sparsity, and is difficult to capture real intentions of users and correlation between sequences; the method effectively improves the accuracy and reliability of a recommendation system, provides more accurate recommendation services for users, and has significant application value in the field of sequence recommendation.

The invention discloses an article recommendation method based on RAG and knowledge graph guidance. The method specifically comprises the following steps: preprocessing and warehousing a structured abstract based on a large language model, adopting an offline processing mode of first abstracting and then warehousing, and utilizing the large language model LLM to obtain a structured abstract document through a two-stage prompt project; performing deep normalization on user input by utilizing the knowledge graph, converting all non-standard input into standardized entity names, and complementing the association relationship of the non-standard input and the standardized entity names; acquiring an article by adopting a sparse vector and dense vector mixed recall strategy; independent reordering and scoring links driven by a Qwen3 32B large model are introduced, a context-aware paired comparison task is designed for the large language model LLM, and a final recommendation result is ensured. The method has the advantages that it is ensured that recommended content is highly consistent with user requirements; professional recommendation services far beyond general schemes are provided; and the quality of recommendation results is greatly improved.