Social association cloud media collaborative filtering and recommending method

Abstract

The invention relates to a social association cloud media collaborative filtering and recommending method. The method includes the following steps that micro blogs sent by multiple micro blog users and associated users of the micro blog users are obtained; a user program rating matrix for reflecting the corresponding relation between different users and grading of different programs is built; influence grading of the associated users on the programs is calculated; the feature vector of the micro log users is calculated; feature similarity of the micro log users is calculated; the influence grading of similar users similar to the micro log users on the programs is calculated; the user program grading matrix is updated according to the influence grading of the associated users on the programs and the influence grading of the similar users on the programs; network resources are explored, and the updated user program grading matrix is expanded; cluster is conducted on the user program grading matrix based on the users and the programs respectively; class cluster obtained through the cluster serves as a neighbor search domain, and grading is predicted through collaborative filtering and recommending. By means of the method, network information content which interests the users can be accurately recommended for the users.

Description

technical field [0001] The invention relates to the technical field of network information push, in particular to a social cloud media collaborative filtering recommendation method applied to a social network. Background technique [0002] The purpose of the recommendation system is to establish a connection between users and information. On the one hand, it helps users find information that is meaningful to them; . Through the analysis of user data in social networks, users' topic interests and trust relationships among users can be obtained. And the corresponding media service provider can recommend books, audio and video, products, etc. to users in a targeted manner through this analysis. For the information provider, this improves the accuracy of pushing information, and for the user, more accurate information can be obtained, helping the user to improve the efficiency of obtaining information. [0003] The basic assumption of the collaborative filtering recommendatio...

AI Technical Summary

Problems solved by technology

Aiming at the problem of information overload, although the traditional personalized recommendation technology has been relatively mature, with the complexity of the recommendation environment, the continuous growth of massive information data in the system, and the continuous improvement of user needs, the traditional personalized recommendation technology has become increasingly popular in social media platforms. The application ignores the user's social relationship, which not only makes it difficult to guarantee the accuracy of the recommendation, but also has problems such as difficult management and analysis

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