Tencent Technology (shenzhen) Company Limited

Kaikai Ge

Xu Zhang

Leyu Lin

Yudan Liu

Linyao Tang

Qian Wang

Jing Zhang

Xin Chen

Su Yan

Kai Zhuang

Xiaobo Hao

Ruobing Xie

Zhida Pan

This application discloses a content recommendation method performed at a computer device and belongs to the field of artificial intelligence. The method includes: acquiring a target user vector of a target user; determining n groups of seed user vectors according to the target user vector, each group of seed user vectors corresponding to a respective piece of candidate recommendation content; invoking a look-alike model to calculate a similarity between the target user vector and each group of seed user vectors, the look-alike model being used for calculating a similarity between user vectors based on an attention mechanism; and determining, among the n pieces of candidate recommendation content, target content to be recommended to the target user according to the respective similarities of the corresponding n groups of seed user vectors. This application can resolve a problem of relatively low accuracy of a recommendation method in the related art.

Content recommendation method and apparatus, device, and storage medium

A method is provided for determining a social rank of a node in a social network, the social network including a plurality of nodes connected by relationship chains. The method includes determining a user corresponding to at least one of the plurality of nodes in the social network, determining a connection structure of the relationship chains between the plurality of nodes, and determining, according to the connection structure of the relationship chains between the plurality of nodes, a social rank of the user corresponding to the at least one of the plurality of nodes.

Method and device for determining social rank of node in social network

Embodiments of this application disclose a data recommendation method performed by a computer device. The data recommendation method includes: obtaining a service object set associated with a target user in a plurality of fields in response to a data recommendation request for the target user in a target field, performing cross-field cross-encoding on the plurality of fields and the service object set to obtain a target field interest feature of the target user in the target field; obtaining to-be-recommended service object features of a plurality of to-be-recommended service objects in the target field; and obtaining a target to-be-recommended service object feature matching with the target field interest feature of the target user from the to-be-recommended service object features, and returning a target to-be-recommended service object corresponding to the target to-be-recommended service object feature to the target user.

Data recommendation method and apparatus, computer device, and storage medium

A recommendation method is provided. In the method, a candidate item to be recommended to a social network user is obtained. The social network user has at least two different types of social relationships. For at least one target social object in each of the at least two different types of social relationships of the social network user, attention of each of the at least one target social object in the respective type of social relationship to the candidate item is determined. According to the attention of each of the at least one target social object in the at least two different types of social relationships to the candidate item, a comprehensive attention of the target social objects of the at least two different types of social relationships to the candidate item is determined. According to the comprehensive attention, whether to recommend the candidate item to the social network user is determined.

Recommendation method and apparatus, and storage medium

Growing community of inventors

Shenzhen, China

Kaikai Ge