Collaborative filtering recommendation method of user rating neighborhood information based on fuzzy mechanism

Abstract

The invention discloses a collaborative filtering recommendation method of user rating neighborhood information based on a fuzzy mechanism. The method has the technical scheme that the method comprises the following steps of: 1, obtaining rating information of a user on a project, and creating a rating matrix; 2, calculating the user rating membership according to the rating matrix, and calculating the contribution of the project to the similarity according to the project context information; 3, building user like and unlike similarity according to the rating membership and the similarity membership; 4, carrying out similarity value reduction on users with the small rating number, and building user Jnum similarity; 5, building user final similarity according to the user like and unlike similarity and the user Jnum similarity; 6, selecting first K users with the highest similarity values as reference neighborhood users according to the final similarity, and completing the prediction on target users. Experiment simulation results show that the method provided by the invention has higher recommendation quality than a conventional collaborative filtering algorithm, and can be used for recommending interested projects for the users.

Description

technical field [0001] The invention belongs to the technical field of collaborative filtering recommendation, and in particular relates to a collaborative filtering recommendation method for constructing user similarity based on user scoring neighborhood information based on a fuzzy mechanism, which can be used for network item recommendation. Background technique [0002] The rapid development of Internet technology has aggravated the problem of information overload, and it is difficult for users to find the content they are interested in in the face of massive data. The recommendation system has received widespread attention since it was first proposed in the 1990s. Based on the user's historical behavior information, the system establishes the relationship between users and items, such as products, movies, music, etc., and finds items of interest to users. and recommend it to users. In recent years, recommendation systems have been widely used, such as e-commerce, books...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the existing collaborative filtering recommendation algorithm, and propose a user scoring context information based

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