Interest recommendation method and device, server and storage medium

Abstract

The invention relates to an interest recommendation method and device, a server and a storage medium. The method comprises the following steps that: obtaining the characteristic information of a target user; according to the characteristic information, adopting an interest similarity prediction model to predict an interest similarity between the target user and a user group, wherein the interest similarity prediction model is realized on the basis of a tree model coding hybrid linear algorithm; according to the interest similarity, determining a recommended user which has the similar interestwith the target user in the user group; and obtaining the interest list of the recommended user, and creating a recommendation list for the target user. By use of the method, the problem of the cold boot of a traditional collaborative filtering algorithm can be solved, wherein the problem is a recommendation problem when the user does not have enough watching history. The accuracy of a recommendation result is improved, a click rate is obviously improved, and a purpose of "everyone has their own view" of personalized recommendation is realized.

Description

technical field [0001] The present invention relates to the technical field of the Internet, in particular to an interest recommendation method, device, server and storage medium. Background technique [0002] Video recommendation has become an integral part of online video services. Existing video recommendation algorithms are mainly divided into non-personalized recommendation algorithms and personalized recommendation algorithms, please refer to figure 1 , the data used by these algorithms mainly include user portraits, user viewing records, and video attributes and other information. Among them, the non-personalized recommendation algorithm includes popularity-based (Popularity) video recommendation algorithm and user grouping based on artificial statistical information combined with popularity-based video recommendation. Personalized recommendations mainly include related recommendations based on the user's current viewing video and personalized recommendations based ...

AI Technical Summary

Problems solved by technology

The common shortcomings of these algorithms are the problems of cold start and data sparsity, that is, when users do not have enough viewing history, traditional video recommendation algorithms are often unable to meet the needs of users

[0004] For collaborative filtering and content-based recommendations, users’ historical viewing behavior data are needed. These methods cannot be used or have poor results for new users or users with less behavior. These problems will affect the user experience and affect user stickiness and video quality. Long-term development of services

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