10results about How to "Efficient recommendation" patented technology

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

The invention belongs to the technical field of recommendation, and particularly relates to a Web API recommendation method based on deep learning enhancement, in particular to a Web API recommendation enhancement multi-task fusion model (EMTFM-BERT) based on BERT and oriented to mixed applications. According to the method, multi-model fusion and multi-task learning are combined with a BERT pre-training model and a fine tuning technology thereof. The proposed solution is divided into three key stages: 1, constructing a recommendation model; secondly, training the model by using the usage data of the Web API and the hybrid application collected from the service provider; and thirdly, processing natural language input submitted by a developer or a user. Based on these inputs, the system generates a set of recommended Web APIs and proposes relevant tags or categories that meet specified requirements.

The present invention discloses an item recommendation method and device, and relates to the field of computer technology. A specific implementation of the method includes: determining an item candidate set of the current user based on historical behavior data of several users; wherein the item candidate set includes several candidate items; for each candidate item: determining several similar items corresponding to the candidate item; generating a recall item candidate set of the current user based on each candidate item and its corresponding similar items; determining several target items based on the recall item candidate set and several preset items to be recommended; and recommending the several target items to the current user. This implementation can effectively recommend the items to be recommended that the user is interested in to the user, and the recommendation effect is good.

This application discloses a refrigerant recommendation method, apparatus, electronic device, and computer-readable medium, relating to the field of logistics and transportation technology. The method includes: acquiring cold chain order data; extracting temperature zones, timeliness, and warehouse locations from the cold chain order data; querying temperature zone configurations based on warehouse location and current time to determine the corresponding temperature zone; determining a corresponding basic scheme based on the temperature zone, timeliness, and temperature zone query scheme group configuration, wherein the basic scheme includes specific refrigerant combinations, dosage quantities, and auxiliary consumables; and outputting refrigerant recommendation results based on the basic scheme upon successful verification of the basic scheme. Through hierarchical management of the configuration, structured storage and efficient matching of refrigerant recommendation rules are achieved. Multi-dimensional intelligent matching is employed, comprehensively considering multiple factors such as temperature zones, timeliness, and temperature levels, to achieve efficient and accurate refrigerant recommendation, improve cold chain order fulfillment efficiency, reduce product damage rates due to improper refrigerant dosage, and enhance product quality assurance capabilities.

The invention discloses an AI-based personalized insurance product recommendation method and system, and mainly relates to the technical field of application of artificial intelligence in insurance products. Comprising the following steps: collecting basic information and behavior data of a user, and preprocessing the collected basic information and behavior data; dividing the users into new users and old users according to a preprocessing result, and calculating an insurance product recommendation result according to the preprocessed basic information and behavior data of the users for the new users; for old users, analyzing product feature tags selected by the users, and recommending similar products through semantic analysis and feature matching; and performing user behavior sequence analysis on all users through the deep learning model, and predicting potential demands of the users. The method has the advantages that accurate mining and intelligent matching of complex data are achieved, meanwhile, the cold start problem is solved, and efficient recommendation is achieved.

The present invention discloses an artificial intelligence recommendation method, system and storage medium based on auto repair ERP, which relates to the field of data management technology, including an auto repair ERP management platform receiving a vehicle maintenance request, determining the maintenance type, and marking the second-class maintenance and the first-class maintenance respectively; outputting a recommendation list according to the maintenance type, and sorting the recommendation list, obtaining the credit value of the auto repair shop and the reliability of the repair service evaluation of the auto repair shop; receiving a selected instruction, issuing an order containing maintenance information, receiving order confirmation information, and forwarding the auto repair shop information if the auto repair shop confirms the order within a preset time, otherwise outputting the recommendation list again until the maintenance request is completed or accepted by the auto repair shop. The present invention aims to solve the problems that it is difficult for car drivers to obtain high-quality auto repair services, that auto repair data cannot be shared and utilized, that the degree of informatization and digitization is low, that the accessibility of high-quality auto repair services is poor, and that maintenance technology and experience cannot be summarized and modeled.

The invention discloses a one-stop human resource outsourcing intelligent service platform system, comprising a talent pool management module, an interview management module, a project management module, an attendance management module, a salary management module, an intelligent risk management module, and a notification and announcement module. The talent pool management module is used for intelligently screening and managing talent information, the interview management module is used for interview management of employees, the project management module is used for following up and managing projects, the attendance management module is used for formulating and managing attendance strategies, the salary management module is used for calculating and managing personnel salaries, the intelligent risk management module is used for intelligent risk assessment, early warning and analysis, and the notification and announcement module is used for issuing company announcements. The invention discloses a one-stop human resource outsourcing intelligent service platform system, proposes a talent retrieval and recommendation algorithm based on naive Bayes to retrieve and intelligently recommend information in the talent pool, and proposes an enterprise risk assessment algorithm based on artificial intelligence to intelligently assess, analyze and predict enterprise risks.

The present invention discloses a method and system for solving the optimal version of open source components in a software project, which comprises: obtaining a dependency tree of the open source components of a target project; using a backtracking method, starting from each root node of the dependency tree, and traversing and processing each subtree of the dependency tree in turn; to obtain a sorting solution set of the subtree; by comprehensively analyzing the sorting solution sets of all subtrees, to obtain a comprehensive solution set corresponding to the target project; according to the comprehensive solution set, combined with the dependency tree corresponding to each sorting solution recorded above, to parse out the optimal solution set of the open source component push version corresponding to the target project; through the above method, starting from the perspective of the entire target project, on the basis of analyzing the risk of each open source component, all the open source components in the target project are comprehensively provided with an optimal version recommendation set, thereby avoiding invalid open source component version recommendations, making the recommendations more effective, so as to adapt to the current target project.

The embodiment of the invention provides a data processing method and device, equipment, a storage medium and a product, and the method comprises the steps: determining a plurality of target impact factors of a target product for the target product, and enabling the target impact factors to be actions and / or product states which affect the order quantity of the target product; according to the multiple target influence factors, a target task for the target product is determined, the target task comprises at least one subtask and an execution sequence of the at least one subtask, and the target task is indicated by the target influence factors; and recommending the target task to prompt to execute the at least one sub-task according to the execution sequence, so that the accurate target task can be provided for the shop for the target product, the shop is prompted to execute the target task, and the product information of the target product is efficiently recommended.