Network content recall method and device

A technology of network content and long-term characteristics, applied in the field of data processing, can solve the problems of not considering the relationship between users' preference for reading history and the degree of browsing interest is not high

Active Publication Date: 2019-09-17
深圳市雅阅科技有限公司
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the current personalized recommendation methods do not consider the user's preference relations...

Method used

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  • Network content recall method and device
  • Network content recall method and device

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Embodiment Construction

[0037] Embodiments of the present application are described below in conjunction with the accompanying drawings.

[0038] At present, personalized recommendations are mainly based on the user's historical browsing data. Since this type of personalized recommendation does not consider the user's preference for reading history, for example: if the user's historical browsing data includes the user's passive Browsing behavior (such as browsing behavior caused by user misoperation), when making personalized recommendations for the user, the browsing data generated based on this will also be used as the basis for the user’s personalized recommendation, which will lead to the fact that the recommended network content is actually The degree of satisfying users' browsing interests is not high.

[0039] For this reason, the embodiment of the present application provides a network content recall method, the core idea of ​​which is: according to the user's historical browsing data, determ...

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Abstract

The embodiment of the invention discloses a network content recall method and device, and the method comprises the steps of determining a long-term feature group and a recent feature group from the historical browsing data according to the generation time of the elements in the historical browsing data of a user; and determining a long-term coding vector corresponding to the long-term feature group according to the similarity between elements in the long-term feature group; according to the similarity degree between the long-term coding vector and the elements in the recent feature group, obtaining the long-term coding vector, determining a user feature vector corresponding to the recent feature group, therefore, so that the information in accordance with the interest preference browsed by the user in the user feature vector can be highlighted, and the other information is retained to a certain extent. The recommendation candidate item determined by the user feature vector better conforms to the actual browsing interest of the user, and has certain generalization, so that the quality of network content recommendation for the user is improved, and the browsing experience of the user is improved.

Description

technical field [0001] The present application relates to the field of data processing, in particular to a method and device for recalling network content. Background technique [0002] Users can browse all kinds of network content through smart terminals. Such as reading, playing, viewing web content, etc. During the browsing process, the smart terminal or the application of the smart terminal can recommend network content for the user. It is expected to recommend the network content that the user is interested in to the user, so as to improve the browsing experience of the user. [0003] At present, it is mainly based on the user's historical browsing data to make personalized recommendations for users. The user's historical browsing data reflects the network content that the user has browsed, and can represent the user's browsing interest to a certain extent. The main idea of ​​the personalized recommendation method is to extract keywords (Tags) from the articles (wat...

Claims

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Application Information

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IPC IPC(8): G06F16/9535
CPCG06F16/9535
Inventor 丘志杰饶君刘毅张博
Owner 深圳市雅阅科技有限公司
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