129 results about "Recommendation service" patented technology

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 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.

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 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 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.

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.

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.

The invention discloses an agricultural and pastoral whole industry chain scene service intelligent linkage method based on agricultural cycle identification, and relates to the technical field of agricultural informatization. Performing farming cycle identification through a preset behavior-cycle correlation model based on the farming condition data, and obtaining a farming cycle state where the user is currently located; according to the farming cycle state of the user, matching and triggering a predefined chained service rule, and generating an initial service recommendation sequence; the chained service rule adopts a state-event-action structure, a completion event of a previous service action is a condition for triggering a next service action, and a final service recommendation is generated through a preset parameterization model based on an initial service recommendation sequence; the final service recommendation is actively pushed to the user terminal; obtaining execution feedback data of the user terminal on the final recommendation service; and optimizing the behavior-cycle correlation model and / or the parameterized model according to the execution feedback data. According to the invention, the accuracy, controllability and intelligent level of agricultural services are improved.

Embodiments of the present specification provide a recommendation processing method and device, wherein a recommendation processing method comprises: obtaining an access instruction of an access user triggering service content recommendation of an application; detecting whether there is a recommendation record of service content recommendation based on a service sequence of the access user; the service sequence is determined in a sequence pool according to an access parameter obtained by performing access parameter prediction on the access user; if yes, determining a to-be-recommended service in the service sequence according to the recommendation record; and recommending service content of the to-be-recommended service to the access user in the application to respond to the access instruction.

Embodiments of the present application provide a split-screen interaction method and an electronic device. The method is applied to an electronic device. The method comprises: displaying a first split-screen interface and a second split-screen interface, the first split-screen interface displaying content of a first application, the second split-screen interface displaying content of a second application, and the first application being different from the second application; and, in response to an operation by a user in an interaction region of the first split-screen interface, the electronic device displaying on the second split-screen interface second content associated with first content displayed in the interaction region, and / or the electronic device displaying on the second split-screen interface a recommended service associated with the first content displayed in the interaction region. By means of the method or the electronic device, interaction content selected by the user in one split screen can be sensed by the other split screen, and a response can be made to the interaction content in the other split screen, so that the efficiency of split-screen processing of a plurality of tasks by the user can be improved and the split screen use experience of the user can be improved.

In embodiments, a method includes dynamically detecting a current revision to a software service running within a container and identifying whether the current revision specifies a significant change or not. In response to the identification, the method further includes selecting a time window in which to analyze the service's usage patterns (the “analyzed time window”) and determining whether to include data regarding the service's usage patterns prior to the current revision in the analyzed time window or not. The method further includes recommending a right-sizing implementation for the service based on the usage patterns in the analyzed time window. In embodiments, until data for the entire analyzed time window has been acquired, the method only implements right-sizing recommendations that increase resources available to the service. Once such data has been acquired, the method implements right-sizing recommendations that both increase and decrease such resources.

The invention relates to a multimedia information display method and device, electronic equipment and a readable storage medium. The method comprises the following steps: in response to a recording operation, obtaining at least one of representative information and a trigger control corresponding to a target multimedia file, and summary information and recommendation service corresponding to the target multimedia file; and displaying a display page including at least one of the representative information and the trigger control, the summary information, and the recommendation service. By adopting the method, the convenience of multimedia information interaction can be improved.

The application discloses a kind of federal social recommendation method and device based on adaptive diffusion denoising, belong to personalized recommendation technical field in electronic commerce and social platform. For the problem of serious noise interference, server load is too high and slow response in existing social federal recommendation, the application constructs interaction graph and social graph by collecting user interaction and social data, initializes user and project embedding representation, carries out adaptive diffusion denoising process in client local, wherein noise scheduling is dynamically adjusted according to user social structure characteristics, and through federal training framework, client local model updating and server global parameter aggregation are realized, finally, denoised social embedding and user interaction embedding are fused to carry out recommendation score prediction and list generation. The application effectively suppresses social noise interference, improves recommendation accuracy and robustness, reduces server load and communication overhead, and is suitable for personalized recommendation service of electronic commerce and social platform.

Embodiments of the present application disclose an information display method and device. The main technical solution comprises: in response to a request for a recommendation service page, obtaining recommendation index data corresponding to each time point in a target period, the recommendation index data corresponding to a time point comprising a recommendation index value of a recommendation task corresponding to the time point, the target period comprising a time point corresponding to at least one target recommendation task, the target recommendation task being a recommendation task whose recommendation index value meets a first requirement; displaying the recommendation service page, the recommendation service page comprising a first page element, the first page element being generated by using the recommendation index data corresponding to each time point in the target period, and information of the target recommendation task being highlighted in the recommendation service page. Embodiments of the present application can help a work producer to quickly know a recommendation task with good recommendation effect, and then efficiently produce a work with recommendation.

The invention discloses a display method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the following steps: receiving a first input on a program interface; in response to the first input, displaying a screen entity service component corresponding to each screen entity in the program interface; wherein the program interface comprises at least one screen entity, the screen entity service component corresponds to one recommendation service, and the recommendation service corresponding to the screen entity service component is generated based on entity information of one screen entity.

The invention relates to a parking management server and a method thereof. The parking management server includes a processor and a memory storing one or more programs executed by the processor, and the processor may determine whether the vehicle is parked, set an available parking time related to a charging output of the vehicle, count elapsed parking times by taking into account a time at which the vehicle is parked for charging, and determine whether the vehicle is parked. And selectively providing a vehicle movement recommendation service to a user of the vehicle based on the available parking time, the elapsed parking time, and a signal for determining whether the vehicle moves.

The application relates to a privacy protection recommendation method and system based on matrix decomposition and belongs to the technical field of privacy protection recommendation. The method comprises the following steps: obtaining a historical rating matrix of a user; performing matrix decomposition on the historical rating matrix to obtain a shared rating matrix; sending the shared rating matrix and a scored item set to a recommendation server to generate public parameters, a decomposition matrix and a shared value of the decomposition matrix; generating inner product triplets and multiplication triplets by using a triplet server; calculating errors between real ratings and predicted ratings by using the recommendation server to generate an error shared matrix; optimizing the error shared matrix until the error squares of all the error shared matrices are less than a threshold value; calculating partial derivatives of a loss function on the decomposition matrix to obtain a gradient shared matrix; updating the decomposition matrix to obtain a user decomposition shared matrix; and generating a privacy protection recommendation result of the user by using the user decomposition shared matrix. The application provides strong protection in the aspect of protecting user privacy.

This invention relates to a localized differential privacy recommendation method and system that balances algorithmic fairness, belonging to the fields of computer recommendation systems and information security technology. The method first divides the dataset into three groups—active, moderate, and inactive—based on user activity. The server initializes and pre-trains the item latent factor matrix, saving the latent factor matrices for each group. Gradient updates and dynamic gradient clipping are performed on each group, adaptively adding noise based on a clipping threshold. In each iteration, the gradient clipping threshold and noise scale are updated. Each group uploads the perturbed item gradients to the server for secure aggregation. Finally, the recommendation server uses the updated item latent factor matrix and distributes it to the local group for further training. After iteration, users generate a top-K recommendation list through local prediction. This invention effectively protects user privacy in scenarios where the server is untrusted, significantly alleviates the performance differences in recommendations between user groups with different activity levels, and achieves a balance between privacy protection and algorithmic fairness.

Based on the user's game execution information, it is possible to recommend appropriate music content to the user. [Solution] The information processing system (10) includes a game device (12), a game history server (16), a recommendation server (18), and a user terminal (20), all of which are connected via a network (14) for communication. When a user plays a game on the game device, game execution information is generated and registered with the game history server. The game history server sends the game history in a data format to the recommendation server in response to a request from the recommendation server. The recommendation server generates recommendation information for music content based on the game history and sends the recommendation information to the user terminal. The user terminal displays a home screen (100) in the music application where the user can select music content recommended based on the recommendation information.

The invention belongs to the technical field of natural language processing and risk management, and discloses a GRPO-RAG-based chemical risk field management and control measure generation method and system, and the method comprises the step of providing a GRPO-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 GRPO-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.

The invention discloses a recommendation information determination method and device, a storage medium and an electronic device, and relates to the technical field of commodity recommendation, and the method comprises the steps: receiving a delivery request through a first channel interface of a transaction channel, executing a delivery operation according to the delivery request, and obtaining order data generated when the delivery operation is executed, the order data at least comprises object data of transaction objects and commodity data of transaction commodities; a recommendation request is received through a second channel interface of the transaction channel, recommended commodities are obtained from the commodity data according to recommendation parameters carried by the recommendation request, recommendation objects of the recommended commodities are obtained from the object data, and the recommendation parameters at least comprise channel numbers; and determining the recommendation information according to the recommendation object and the recommended commodity, and by adopting the technical scheme, the technical problems that the recommendation service of the commodity provider is difficult to effectively expand to the existing channel and the accuracy of commodity recommendation for the user on the channel side is not high are solved.