Content recommendation method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a content recommendation method and device, electronic equipment and a storage medium, and relates to the field of data analysis. The method comprises the following steps: acquiring basic information and behavior information of a user and attribute information of a plurality of contents; determining the similarity among the contents according to the attribute information of the contents, and generating a plurality of recall sets according to the similarity, each recall set comprising a plurality of contents of which the similarity is greater than a preset threshold; generating a label and a label weight of the user according to the basic information and the behavior information; screening out at least one target recall set from the plurality of recall sets according to the labels of the users; and screening contents in the target recall set according to the label weight of the user, and generating and outputting a content recommendation result according to a screening result. According to the content recommendation method provided by the embodiment of the invention, the accuracy of recommending the content to the users without clear requirements can be improved, and the retention time of the part of users can be prolonged.

Description

technical field [0001] The present application relates to the technical field of data analysis, in particular, to a content recommendation method, device, electronic equipment and storage medium. Background technique [0002] With the development of the Internet, people have entered the era of information explosion. In order to meet people's demand for information, information websites have emerged as the times require. Information websites usually divide the collected information into multiple types, and users can freely choose the type, so as to conveniently obtain the information they are interested in. [0003] In the existing technology, many information websites use collaborative filtering algorithms to calculate the content that users may be interested in and recommend them to users. Collaborative filtering algorithms simply use the preferences of similar user groups to recommend users that they may be interested in. content, so as to improve the retention time of us...

AI Technical Summary

Problems solved by technology

[0004] However, collaborative filtering algorithms are more dependent on similar user groups, and it is difficult to accurately recommend content to users who do not have clear needs, that is, users who do not have similar user groups, and it is easy to cause the retention time of these users to be too short or even lost

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