8results about How to "Improve recommendation effect" patented technology

This application provides a video recommendation method, apparatus, device, and storage medium, relating to the field of communication technology. The method includes: upon receiving a video acquisition request, acquiring a candidate video list; acquiring the interaction probability between the user and each candidate video in the candidate video list, the interaction probability being determined based on the user's reconstructed embedding vector and the candidate video's reconstructed embedding vector; the user's reconstructed embedding vector being determined based on the user's embedding vector, the user's feature vector, and a pre-trained vector reconstruction model; the candidate video's reconstructed embedding vector being determined based on the candidate video's embedding vector, the candidate video's feature vector, and the vector reconstruction model; simulating the input of sample data during cold start when training the vector reconstruction model; and determining the target video to recommend to the user from the candidate video list based on the interaction probability between the user and each candidate video in the candidate video list. This method can accurately recommend videos to users during cold start, improving the video recommendation effect during cold start.

The application discloses a user sensitive information protection method based on a data confusion technology, and comprises the following steps: step S1, generating a sensitive attribute association table based on the association between recommended items and user gender characteristics; step S2, adding confusion ratings to an existing user-item matrix through a sampling strategy according to the sensitive attribute association table obtained in step S1, and constructing a user-item matrix after adding the confusion ratings; step S3, recording the number of confusion ratings added in step S2, applying a removal strategy, and finally generating a confusion matrix after applying the removal strategy; the removal strategy is that the same number of confusion ratings are deleted to maintain the original data size; a user is randomly selected, and when the rating number of the user reaches a set threshold, the items with strong association of the opposite sex are deleted from the rating history of the user. The application can mislead attackers to conduct gender attribute inference, realize user privacy protection, and does not affect the existing personalized recommendation quality of the user.

This invention provides a video recommendation method, apparatus, electronic device, and storage medium. It generates a user status document based on multi-source heterogeneous user information, and then calls a large language model to generate a first semantic unit based on the user status document. Videos matching the first semantic unit are searched from a vector database, and at least a portion of these videos are recommended to the user, thereby improving the video recommendation effect.

Embodiments of the present application disclose a content recommendation method and device, computer equipment and a computer readable storage medium. Based on a plurality of historical interaction contents of a target object, content attribute information sequences under a plurality of attributes are obtained, each content attribute information sequence including attribute information of the plurality of historical interaction contents under the same attribute. The content attribute information sequences are subjected to attention feature mining processing to obtain an attention feature information sequence corresponding to each attribute. The attention feature information sequences are subjected to first feature fusion processing to obtain a fused attention feature information sequence. A behavior feature information sequence of the target object is generated according to a target content attribute information sequence belonging to a content identifier attribute in the content attribute information sequences. The fused attention feature information sequence and the behavior feature information sequence are subjected to second feature fusion processing to obtain object feature information of the target object. The object feature information is used to recommend content to the target object, and the content can be accurately recommended.

The application relates to an Issue fixer recommendation method and device, equipment and a readable storage medium, relates to the open source software ecosystem field and the service recommendation technical field, and fully considers the cooperative interaction relationship between developers and Issues, that is, a cooperative interaction graph between developers and Issues is constructed according to the cooperative relationship between developers, the interaction behavior between developers and Issues and the similar relationship between Issues, feature learning is performed on the developers and Issues, and the fixer recommendation is performed by using the inner product result of the feature vectors of the developers and Issues, so that the fixer recommendation result is more comprehensive and accurate, and the Issue fixer recommendation performance is effectively improved.

This disclosure presents embodiments of a method, apparatus, device, medium, and program product for generating material sequences. One specific implementation of the method includes: in response to the existence of target creative materials in a set of creative materials to be recommended, determining a target set of creative materials to be recommended; for each target creative material to be recommended, selecting a predetermined number of target historical recommended creative materials from a set of historical recommended creative materials; generating an estimated click-through rate (CTR) for each target creative material to be recommended based on the CTR of each target historical recommended creative material; determining the CTR of each multiple recommended creative material; and sorting the creative materials to be recommended to obtain a sequence of creative materials to be recommended. This implementation is related to artificial intelligence; by determining the CTR corresponding to the target creative materials to be recommended, a more accurate material recommendation order can be generated for the set of creative materials to be recommended, improving the material recommendation effect.

The application provides a test question recommendation method and device, an electronic device and a storage medium. The method comprises: generating recommended test questions corresponding to target test questions based on the target test questions corresponding to a target student and test question recommendation strategies corresponding to the target student; wherein the test question recommendation strategies are determined based on a first test question recommendation strategy and a reference test question recommendation strategy, the first test question recommendation strategy comprises a test question recommendation strategy of a target region to which the target student belongs, and the reference test question recommendation strategy comprises at least one of a test question recommendation strategy of an associated region of the target region and a general test question recommendation strategy. The technical solution of the application can improve the test question recommendation capability of the test question recommendation strategy without sharing the test question library of the associated region, and can improve the accuracy of test question recommendation while ensuring the data privacy and security of the test question resources of each region by combining the test question recommendation strategies determined by the test question library of the associated region and / or the general test question library of the region.

The application provides a commodity recommendation method, device, system and storage medium, and belongs to the commodity recommendation field.The method comprises the following steps: constructing user nodes, user node data, commodity item nodes and commodity item node data through user information and commodity information; updating the user node vector of the user nodes and the user node data to obtain an updated user node vector; and updating the commodity item node vector of the commodity item nodes and the commodity item node data to obtain an updated commodity item node vector.The application can capture global collaborative features and social influences between friends, can effectively filter node transition relationships irrelevant to current sequence transactions, and can further improve recommendation performance and accuracy.