Tencent Technology (Shenzhen) Company Limited

Jun Rao

Yi Liu

Hua Li

Zhijie Qiu

Lei Chen

Liangdong Wang

Shukai Liu

Zhenlong Sun

Qi Liu

Leyu Lin

Tiantian Shang

Zhou Su

Mingfei Liang

Bo Zhang

Feng Xia

Zhe Feng

Ruobing Xie

Lantao Hu

Zhi Jie Qiu

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

The present disclosure provides a data processing method performed by an electronic device. The method includes: obtaining first and second service behavior features, and a service scenario feature of an object from service history data associated with the object; generating first and second service embedding vectors and a scenario representation vector according to the first and second service behavior features and the service scenario feature; obtaining first and second weights of the first and second service embedding vectors according to the scenario representation vector and the first and second service embedding vectors; generating first and second service feature vectors according to the first and second service embedding vectors and the first and second weights of the first and second service embedding vectors, respectively; and obtaining an object embedding vector according to the first service feature vector and the second service feature vector.

Data processing methods and systems for determining a service target item

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 method and an apparatus for processing web content, a device, and a computer storage medium are provided. A long-term feature group and a short-term feature group are determined from historical browsing data of a user according to generation time points of elements in the historical browsing data. A long-term encoding vector corresponding to the long-term feature group is determined according to similarities between elements in the long-term feature group, a user embedding vector corresponding to the short-term feature group is determined according to the long-term encoding vector and similarities between elements in the short-term feature group, and at least one web content is determined as a recommendation candidate and provided to the user.

Method and apparatus for processing web content, device, and computer storage medium

A video recommendation method is provided, including: inputting a video to a first feature extraction network, performing feature extraction on at least one consecutive video frame in the video, and outputting a video feature of the video; inputting user data of a user to a second feature extraction network, performing feature extraction on the discrete user data, and outputting a user feature of the user; performing feature fusion based on the video feature and the user feature, and obtaining a recommendation probability of recommending the video to the user; and determining, according to the recommendation probability, whether to recommend the video to the user.

Video recommendation method and device, computer device and storage medium

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Shenzhen, China

Jun Rao