Tencent Technology (shenzhen) Company Limited

Leyu Lin

Xu Zhang

Feng Xia

Qian Wang

Su Yan

Yudan Liu

Linyao Tang

Kaikai Ge

Jing Zhang

Xin Chen

Zhe Feng

Kai Zhuang

Lei Chen

Yi Liu

Rui Wang

Jun Rao

Zhijie Qiu

Ruobing Xie

Shukai Liu

Zhenlong Sun

Liangdong Wang

Xiaobo Hao

Yalong Wang

Hua Li

Shuo Wang

Bo Zhang

Lixin Zhang

Qi Liu

Zhiping Wang

Junhong Yan

Yang Cui

Zhiwei Guo

Yang Zuo

Ming Zou

Ge Wang

Jianxiong Feng

Xiumin Lin

Jiayi Ding

Zhou Su

Tiantian Shang

Mingfei Liang

Chenglin Zhong

Shanpeng Sun

Mu Yuan

Lantao Hu

Xiangcong Zeng

Cheng Ling

Lihuan Du

Zhida Pan

Qihao Zhu

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

Embodiments of this application provide a method for making recommendations to a user and an apparatus, a computing device, and a storage medium. The method includes obtaining user attribute information, reading attribute information, reading history information, and candidate items; performing intra-group information fusion on the reading attribute information according to preset groupings to obtain reading feature information; obtaining a reading history weight according to the reading history information; obtaining history feature information according to the reading history weight and the reading history information; obtaining user feature information according to the user attribute information, the reading feature information, and the history feature information; and selecting a recommendation item from the candidate items according to the user feature information.

Method for making recommendations to a user and apparatus, computing 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

Embodiments of the present disclosure relate to a content processing method and apparatus, a computer-readable storage medium, and a computer device. The method includes obtaining data of a to-be-pushed user in behavior evaluation dimensions, user portrait information of the to-be-pushed user, and to-be-pushed content, with the user portrait information corresponding to a user portrait code and the to-be-pushed content corresponding to a to-be-pushed content code. The method further includes determining a user behavior code based on the data of the to-be-pushed user in the behavior evaluation dimensions, and obtaining a target code by fusing the to-be-pushed content code with the user behavior code and the user portrait code. Then, the method includes determining a recommendation probability of the to-be-pushed content based on the target code to determine the push content based on the recommendation probability.

Content processing method and apparatus, computer-readable storage medium and computer device

This application provides a content recommendation method and apparatus, an electronic device, and a storage medium. The content recommendation method includes obtaining content feedback information of a target object and content feature information of content that is to be recommended in response to a content recommendation request of the target object, the content feedback information comprising explicit feedback information and implicit feedback information and object portrait information of the target object; performing feature interaction according to the explicit feedback information and the implicit feedback information in the content feedback information, and obtaining behavior preference information; performing feature extraction based on the behavior preference information, the content feedback information, and the content feature information, and obtaining a predicted click-through rate (CTR); and determining, according to the predicted CTR, recommended content from the pieces of content that is to be recommended, and transmitting the recommended content to a terminal device.

Content recommendation method and apparatus, electronic device, and storage medium

This application provides an article recommendation method and apparatus, a computer device, and a storage medium. The method includes: obtaining a historical reading article set of a to-be-analyzed target user, the historical reading article set comprising a plurality of historical articles that have been read by the target user at different reading moments; determining, for each historical article of the plurality of historical articles, first attention degree values between the each historical article and candidate reference historical articles having reading moments before that of the each historical article, the first attention degree values reflecting a possibility that a user is recommended to read the historical article if the user has read the candidate reference historical articles; selecting at least one recommendation reference article from the candidate reference historical articles corresponding to the historical articles according to the first attention degree values between the historical articles and corresponding candidate reference historical articles; and determining, in a recommendable article set at least one candidate recommended article to be recommended to the target user based on the at least one recommendation reference article.

Article 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

Embodiments of the present invention disclose a result ranking method and device. The method includes: acquiring search key-information by using an interaction application, and searching a prestored interaction data set for at least one search result associated with the search key-information; ranking the at least one search result according to a quality assessment score corresponding to each of the at least one search result; and outputting the ranked at least one search result. The quality assessment score corresponding to each search result is a value generated according to a number of historical operations performed on each search result and an interactive influence score of an application identifier performing a historical operation on each search result.

Search result ranking method and device

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

Leyu Lin