Collaborative filtering recommendation method based on explicit trust and implicit trust

Abstract

The invention provides a collaborative filtering recommendation method based on explicit trust and implicit trust, and the method comprises the steps: calculating the prediction scoring accuracy of auser u for a user v according to a user article scoring matrix R; calculating the degree of dependence of the user u on the user v; calculating the implicit trust degree of the user u to the user v according to the accuracy and the dependence degree; filling the user article scoring matrix R according to the implicit trust degree; calculating the scoring confidence of the user u to the article; calculating the similarity between the user u and the user v according to the scoring confidence coefficient and the filled user article scoring matrix; calculating the global trust degree of the user uand the local trust degree of the user u to the user v; calculating the final trust degree of the user u to the user v according to the global trust degree and the local trust degree; correcting a potential characteristic user matrix of the user u; and according to the potential user feature matrix, predicting the score of the user u on the article i. The problems of data sparseness and user coldstart caused by information overload can be solved, and the recommendation accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of recommendation, in particular to a collaborative filtering recommendation method based on explicit and implicit trust. Background technique [0002] Social media is an important platform for people to communicate and obtain information. Social media users can follow friends, comment on friends, forward comments, browse hot news, etc. on this platform. Social media will record user behavior data. Due to the huge number of social media users, user behavior information is massive, such as Sina Weibo, Toutiao’s attention and browsing information, Epinions (Epinions is a mass consumer Trust information in review sites, etc. [0003] Same as in the real world, in social media, users influence each other's choices and recommend each other's favorites. For example, machine learning is so hot now, and people want to know the latest research status of "machine learning", we usually first get to know the experts ...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a collaborative filtering recommendation method based on explicit and implicit trust, the purpose of which is to solve the problem of data sparsity and user cold start caused by information overload

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