Tencent Technology (shenzhen) Company Limited

Su Yan

Qian Wang

Xin Chen

Xu Zhang

Leyu Lin

Jing Zhang

Yudan Liu

Kaikai Ge

Linyao Tang

Kai Zhuang

Shuo Wang

Feng Xia

Junhong Yan

Zhiping Wang

Yang Cui

Zhiwei Guo

Zhe Feng

Yang Zuo

Ming Zou

Jianxiong Feng

Ge Wang

Xiumin Lin

Xiaobo Hao

Jiayi Ding

Qihao Zhu

Chenglin Zhong

Shanpeng Sun

Zhida Pan

Xulin Liao

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

This application relates to a user tag generation method performed by a computer device, relating to the field of neural networks. The method includes: obtaining discrete user data corresponding to a target user identifier in multiple feature fields respectively; for each feature field, obtaining an intra-field feature corresponding to the target user identifier according to the discrete user data in the feature field; merging the intra-field features to obtain an inter-field feature corresponding to the target user identifier; performing feature crossing on sub-features in the inter-field feature to obtain a cross feature corresponding to the target user identifier; and selecting, from candidate user tags, a target user tag corresponding to the target user identifier according to the inter-field feature and the cross feature. The solutions provided by this application can improve the accuracy of generating user tags.

User tag generation method and apparatus, storage medium, and computer device

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

This application discloses an information recommendation method and apparatus, a device, and a storage medium, and belongs to the field of information recommendation. The method includes starting an application program according to a start operation, a first account being logged in in the application program; obtaining a recommended information flow for the first account, recommended information in the recommended information flow including at least one piece of interactive recommended information, the interactive recommended information being information for which a second account generates an interactive message, the first account and the second account having a social relationship; and displaying an information presentation interface, the information presentation interface comprising the recommended information displayed in an information flow form.

Information recommendation method and apparatus, device, and storage medium

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Shenzhen, China

Su Yan