215 results about "Recommendation service" patented technology

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 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 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 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 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.

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.

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 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.

The invention relates to the technical field of vehicle service recommendation, and discloses a vehicle historical path and user behavior fused service recommendation method and system, and the method comprises the steps: obtaining a vehicle historical path, obtaining a generated path mode portrait, fusing vehicle information into the generated path mode portrait, and obtaining a vehicle travel feature set; carrying out clustering processing on the vehicle travel feature set based on a clustering analysis algorithm, identifying different travel scene categories, establishing a correlation model between travel scenes and behavior modes, obtaining group behavior mode features, constructing a mapping relation between a feature space and a behavior space, and obtaining a service recommendation mapping model; and based on the geometric features of the candidate paths and the service recommendation mapping model, adopting a multi-objective optimization algorithm to generate an optimal path, collecting feedback data of the user for personalized recommendation, and correcting the service recommendation mapping model based on the feedback data. According to the invention, the vehicle information is combined with the historical path and the user, so that the recommendation service is more humanized.

A system for generating recommended computing resource configurations for machine learning services is described. The system includes computing resources to host a machine learning model. The system includes a machine learning recommendation service to receive, from a client via an interface, a request to monitor the computing resources. The recommendation service monitors the machine learning model, including recording utilization metrics of the computing resources, recording the different inference requests, and recording the respective inferences. The recommendation service generates a recommended computing resource configuration for the machine learning model based on the utilization metrics and an optimization objective for utilizing computing resources. The recommendation service determines to provide the recommended computing resource configuration based on an accuracy analysis performed for the machine learning model deployed on the recommended computing resource configuration. The recommendation service provides the recommended computing resource configuration for the deployed machine learning model.

The invention relates to a long-tail recommendation method and system based on multi-agent collaborative reasoning, and belongs to the technical field of personalized recommendation. The method comprises the following steps: performing text enhancement on user and project data, and constructing a semantic data set; constructing and training a candidate item retrieval model with long-tail perception capability; searching candidate items for a target user, and constructing a deep user portrait containing a personalized novelty exploration coefficient; starting a multi-agent collaborative reasoning process, and rearranging candidate items through multi-dimensional evaluation, strategy sorting and adaptive causal depolarization; and finally, generating a personalized recommendation list, and performing iterative optimization based on user feedback. According to the method, the recommendation task is decomposed into a collaborative reasoning step of a plurality of agents, and a personalized and causal-driven prejudice removing mechanism is introduced, so that the accuracy of overall recommendation is not damaged while the exposure and recommendation diversity of the long-tail project are improved, and a more fair and transparent recommendation service conforming to the real exploration intention of the user is provided for the user.

The invention discloses an intelligent distribution system based on deep learning and dynamic resource scheduling, and relates to the technical field of resource scheduling. Comprising the steps of 1, creating an intelligent distribution system, 2, integrating an NLP engine through a user request analysis module, carrying out intention recognition and semantic analysis on a user request, extracting a service type and related parameters, carrying out triple positioning by fusing GPS / base station / Wi-Fi, and obtaining user position information, and 3, constructing a service personnel digital portrait library through a dynamic resource management module, 4, calculating the geographic proximity of candidate service personnel and the user position through an intelligent matching module by adopting a Manhattan distance formula, training an LSTM neural network based on historical service data by utilizing a service quality prediction model, and obtaining the current position and the moving speed of the service personnel through the LSTM neural network; and step 5, obtaining user service evaluation data through a feedback module, triggering a service quality prediction model to perform retraining, and outputting a service quality prediction value in the confidence interval, generating a real-time comprehensive priority according to the geographic proximity, the service quality prediction value and the service emergency degree, and recommending services according to the comprehensive priority, and step 5, obtaining user service evaluation data through the feedback module, and triggering the service quality prediction model to perform retraining. And updating the service personnel digital portrait library by adopting an incremental learning strategy.

The invention discloses a product recommendation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, basic information and demand preferences of a user are obtained, a multi-dimensional dynamic feature vector is constructed, the vector can dynamically capture multiple demands of the user in different stages of a life cycle, and the user experience is improved. The method does not depend on a fixed preset rule or single-dimension data; and generating a recommendation request based on the dynamic vector, and inputting the recommendation request into a recommendation algorithm module to screen and sort products, so that recommendation logic can closely fit real-time demand change of a user, and limitation of static analysis of a traditional model is avoided, and therefore, deviation between a recommendation result and an actual demand of the user caused by an existing preset rule or a traditional machine learning model can be solved; the technical problems that the recommendation accuracy and the user satisfaction are affected are solved, and the technical effects of improving the recommendation accuracy of the personal pension insurance products, effectively matching the actual demands of the users in different stages and improving the satisfaction of the users to recommendation services are achieved.

The invention discloses a commodity recommendation strategy determination method and device, equipment and a medium, and the method comprises the steps: inputting a preset number of user search request data into a large model, and controlling the large model to generate a corresponding standard commodity sorting list; based on each preset sorting factor combination and a preset scoring rule, calculating each sorting factor score of commodities corresponding to the recalled commodity information in each piece of user search request data, and generating a corresponding to-be-evaluated commodity sorting list; calculating a sorting quality evaluation index value between each to-be-evaluated commodity sorting list and the corresponding standard commodity sorting list, and determining a final evaluation index value of each sorting factor combination based on the sorting quality evaluation index value; and according to the final evaluation index value, determining an optimal sorting factor combination as a target recommendation strategy to be applied to the commodity recommendation service of the target shop. By dynamically adjusting the recommendation strategy, the recommendation result can be optimized according to different shop requirements and user behavior data.

The invention discloses a personalized recommendation system and method based on a dynamic causal graph learning framework, and relates to the technical field of personalization. According to the personalized recommendation system based on the dynamic causal diagram learning framework, user interest drift is dynamically captured to balance short-term and long-term preferences, the information cocoon house effect is weakened, and recommendation diversity is improved; the article popularity is debiased by means of causal reasoning, the unfairness of recommendation caused by the Mattai effect is solved, and the recommendation fairness is enhanced; meanwhile, by capturing causal element paths of user preferences and generating interpretable recommendation reasons, an algorithm black box is broken through, recommendation transparency is improved, the personalized requirements of dynamic changes of the user can be precisely met, diversity, fairness and interpretability of recommendation results can be taken into consideration, more comprehensive, reasonable and understandable recommendation services are provided for the user, and the user experience is improved. And meanwhile, a solution considering technical performance and social ethics is provided for sustainable development of the recommendation system.

Videos depicting products are analyzed to uniquely identify the products by frame within each video and to uniquely identify non-product objects by frame within each video. Based on the analysis each video is tagged with product codes and non-product identifiers. Based on the non-product identifiers, each video is further classified by persona. During a checkout of a customer, a recommendation service provides recommended products that the customer is believed to be interested in purchasing. The recommended products and known personas of the customer are used to generate a video playlist for the checkout, each video including at least one of the recommended products presented within the video in a known persona context. A video from the playlist is selected and played within a screen on a display to the customer during the checkout. The screen is a screen not being used by a transaction user interface for the checkout.

Disclosed embodiments provide a framework for automatically generating media promotions according to real-time media analytics and that can be presented to different users of a peer-to-peer music recommendation service. In response to a user query to generate a promotion for a song, the service converts the query into a set of embeddings. Using user profile data and historical music data corresponding to the song and obtained based on the set of embeddings, the service generates music analytics corresponding to the song and recommendations for different music promotions associated with the song. The service can implement these music promotions and track the efficacy of these music promotions as users engage with the music promotions and the song.

The invention discloses a network service recommendation method and system based on user behavior track analysis, and relates to the technical field of data analysis, and the method comprises the steps: collecting a power interaction behavior data sequence, and carrying out the analysis of a user behavior track; user power demand interactive capture prediction is carried out on the temporal behavior track characteristics and the spatial behavior track characteristics obtained through analysis, and a target user power demand prediction result is obtained; performing recommendation scheme analysis on the power network service platform to obtain an initial network service recommendation scheme; and obtaining a real-time position of a target user, and performing scheme deepening analysis in combination with the initial network service recommendation scheme to determine a target network service recommendation scheme. According to the method, the technical problems that user behaviors and demands cannot be accurately captured and recommendation services are lack of individuation and real-time performance in the prior art are solved, and the technical effects of accurately predicting user power demands and providing real-time and personalized power network service recommendation are achieved.

A technique for predicting and recommending service offerings includes obtaining an initial dataset related to a user interaction with an online recommendation system during a user online session, generating a predicted intent of the user online session, and generating an initial set of recommendations based on the predicted intent of the user online session. The technique includes ranking the initial set of recommendations to generate a set of ranked recommendations, generating a predicted sequence of actions of the user, and determining that the ranked set of recommendations are to be re-ranked into a re-ranked set of recommendations. The technique includes generating a semantically customized ranked set of recommendations using at least one of the ranked set of recommendations or the re-ranked set of recommendations and providing the semantically customized ranked set of recommendations to the user.

The invention provides a driving training theory course multi-strategy AI recommendation system and method, specially establishes a set of novel driving training theory course multi-strategy AI recommendation processing architecture, accurately constructs a user portrait through a multi-dimensional user tag system, continuously combines with a multi-model collaborative NLP verbal skill to process an accurately adapted scene, and improves the user experience. According to the driving training theory course multi-strategy AI recommendation processing method and system, the driving training theory courses are recommended according to the driving training theory courses, the recommended driving training theory courses are matched, differentiated strategy output of the recommended courses is achieved, high-quality driving training theory course recommendation services are provided under the personalized driving training theory course multi-strategy AI recommendation processing architecture deeply meeting real requirements, and the driving training theory course multi-strategy AI recommendation processing method and system have good application prospects.

A non-limiting example information processing system comprises a game apparatus, a game history server, a recommendation server and a user terminal respectively connected communicably with each other via a network. If a user executes a game on the game apparatus, game execution information is generated and registered in the game history server. The game history server transmits a game history of a data format to the recommendation server. The recommendation server generates recommendation information of music content based on the game history and transmits the recommendation information to the user terminal. The user terminal displays music content that is recommended based on the recommendation information.

The invention belongs to but is not limited to the technical field of natural language processing and risk management, and discloses a PPO-RAG-based chemical risk field management and control measure generation method and system, and the method comprises the step of providing a PPO-RAG-based management and control measure generation method. In order to solve the problem that LLM has knowledge hysteresis and insufficient user preference adaptability in a management and control measure generation task, a PPO-RAG framework is established by adopting a mode of combining RAG and reinforcement learning based on human feedback. The framework aims to improve the timeliness and speciality of LLM generation management and control measures, so that the LLM generation management and control measures can better meet diversified requirements in actual application scenes. A final experiment result shows that the method effectively improves the comprehensive capability of LLM generation management and control measures. According to the embodiment of the invention, high-quality management and control measure recommendation service can be provided for the user aiming at the characteristics of high professional property and high complexity of the management and control measures in the risk field.