Content recommendation method and device and equipment

Abstract

The invention discloses a content recommendation method and device and equipment. The embodiment of the method includes the steps that feature information, corresponding to multiple preset feature tags, of a user and to-be-recommended content are acquired; based on the feature information and recommendation weights, determined with the analytic hierarchy process, of the preset feature tags, recommendation indexes of the to-be-recommended content are calculated; target recommended content which is determined from the to-be-recommended content according to the recommendation indexes is recommended to the user, wherein the recommendation weights are determined in the way that the preset feature tags are clustered to determine class tags of the preset feature tags; a hierarchical structure model comprising a criterion layer, a sub-criterion layer and a target layer is constructed, wherein elements of the sub-criterion layer are the preset feature tags, and elements of the criterion layer are the class tags; based on the hierarchical structure model, weights, corresponding to the target layer, of the elements of the sub-criterion layer are determined with the analytic hierarchy process and serve as recommendation weights of the preset feature tags. By means of the embodiment, the recommendation efficiency can be improved.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to the field of Internet technology, and in particular to a content recommendation method, apparatus, and device. Background technique [0002] With the development of Internet technology, people can perform more and more activities through the Internet, such as getting news, sending and receiving emails, shopping on online platforms, and so on. In order to improve the efficiency of providing content to users, when a user accesses network resources through the Internet, a website or platform may recommend to the user content that the user may be interested in. [0003] Existing content recommendation methods usually identify content types that users may be interested in based on network behavior data such as users' historical browsing records and search records, and then recommend corresponding types of content to users. However, this method can only determine the type ...

AI Technical Summary

Problems solved by technology

However, this method can only determine the type of recommended content, and cannot further judge whether this type of content contains the content that the user is interested in. The user needs to further filter or browse other types of content among the recommended content.

Therefore, the existing content recommendation methods cannot accurately locate the content that users are interested in, and the efficiency and conversion rate of content recommendation need to be improved.

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