User similarity measuring method in collaborative filtering

Abstract

The invention discloses a user similarity measuring method in collaborative filtering. A relation between user numerical value attributes and user classification attributes is established, a clustering algorithm is adopted for mining user groups, user group information and user scoring information for articles are comprehensively considered, potential interests of users are predicated, and therefore personalized recommendation for the users is completed. The method has the advantages that the accuracy of measuring the similarity among the users can be improved, the precision of mining the interests of the users can be improved, and network user experience can be improved.

Description

technical field [0001] The invention relates to a method for measuring user similarity in collaborative filtering, which is used for realizing high-precision collaborative filtering personalized recommendation for users, and belongs to the technical field of computer networks. Background technique [0002] With the continuous emergence of information and Internet technology, people are facing increasingly serious "information overload" (Information Overload) problem. The Recommendation System is dedicated to helping people find the information they are interested in from the complicated information provided by the Internet. At present, the recommendation system has received extensive attention from researchers, and has achieved a large number of practical applications in various fields such as e-commerce, social networks, and smart TVs. Generally speaking, recommendation systems can be divided into two categories: content-based and collaborative filtering. Among them, colla...

AI Technical Summary

Problems solved by technology

The root cause of this measurement accuracy bottleneck is that the existing methods do not make full use of the user's social attribute information, and this user's social attribute information often better reflects the user's characteristics and the relationship between other users.

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