4results about How to "Improve recommendation quality" patented technology

The application provides a catering recommendation method and system applied to a digital multimedia online platform, relates to the technical field of the digital multimedia online platform, and first acquires user catering interaction intention data and platform internal catering content supply time sequence data; constructs a user catering intention evolution model based on the user catering interaction intention data and the platform internal catering content supply time sequence data, forms an intention evolution link; establishes intention supply time sequence adaptation logic in combination with the model and the supply time sequence data; screens content from a platform catering content library according to the adaptation logic to generate a dynamic recommendation scheme; pushes the dynamic recommendation scheme to a user interface and captures new interaction intention data, integrates the original data to update the model and the adaptation logic, so that the dynamic change of the user intention can be accurately grasped, dynamic recommendation is combined with supply time sequence characteristics, and the user consumption experience and platform competitiveness are improved.

This application discloses a content recommendation method and related products, relating to the field of artificial intelligence technology. The method involves: obtaining a first sequence of operation information of a target object on a specified type of historical content in the current terminal device; obtaining a second sequence of operation information of the target object on a specified type of historical content under the current account; obtaining a first attention enhancement vector for the first operation information sequence through a first attention mechanism; obtaining a second attention enhancement vector for the second operation information sequence through a second attention mechanism; fusing the first and second attention enhancement vectors to obtain an attention fusion vector; performing feature cross-processing on the first, second, and attention fusion vectors to obtain a preference vector; and determining target content to be recommended to the target object from a candidate content library based on the similarity between the candidate content vector and the preference vector. This application can improve the quality of content recommendation.

This invention discloses a method, apparatus, device, and storage medium for determining a course recommendation list. The method includes acquiring sample data; obtaining a target basic data dictionary based on the sample data; obtaining a target sparse vector based on the sample data and the target basic data dictionary; acquiring an initial feature vector of the object to be recommended; obtaining a target feature vector of the object to be recommended based on the target basic data dictionary, the target sparse vector, and the initial feature vector of the object to be recommended; and determining a course recommendation list for the object to be recommended based on the target basic data dictionary and the target feature vector. The technical solution of this invention provides a new method for determining a course recommendation list, fully considering the characteristic information of employees from various dimensions, effectively avoiding the scenario of only recommending courses of personal interest during subsequent course recommendations, and improving the quality and accuracy of course recommendations.

This invention provides a method and apparatus for training a recommended works determination model, relating to the field of data processing technology. The method includes: acquiring historical behavior data of sample users; extracting sample content feature information and sample cover image feature information to obtain sample user feature information; generating sample feature information based on the sample content feature information, sample cover image feature information, and sample user feature information; inputting the sample feature information into a recommended works determination model to obtain an evaluation score; calculating the sample loss of the recommended works determination model based on the evaluation score and the historical behavior data of the sample users; adjusting the parameters of the recommended works determination model based on the sample loss; and returning to the step of inputting the sample feature information into the recommended works determination model until a preset training convergence condition is reached, resulting in a trained recommended works determination model. The solution provided by this invention can be used to train a model capable of recommending works to users.