Collaborative filtering recommending method based on multiple-similarity of users

Abstract

The invention relates to a collaborative filtering recommending method based on multiple-similarity of users. The collaborative filtering recommending method mainly comprises the following steps of firstly determining multiple types included in projects to be recommended in advance, secondly respectively calculating multiple prediction scores of the users based on the types of the projects, more accurately describing similarity degree of favorite of the users on different types of the projects, and getting weighted average of the calculated various prediction scores to be used as final prediction scores of the projects to be recommended to be provided for an existing user, thus effectively improving the accuracy of prediction score.

Description

technical field [0001] The invention relates to a collaborative filtering recommendation method, in particular to a collaborative filtering recommendation method based on multiple similarities between users. Background technique [0002] The advent of the Internet age has added an extremely important way for people to obtain information. The rapid development of Internet technology has also driven the total amount of information on the Internet to increase at an exponential rate, making the problem of Internet information overload increasingly serious. Faced with the vast amount of information, users often become at a loss and cannot quickly find the information they need. As a technical means that can effectively improve the problem of Internet information overload, the recommendation system is receiving more and more attention from the industry. Since it was proposed in the mid-1990s, it has gained great popularity both in academia and in practical applications. focus on...

AI Technical Summary

Problems solved by technology

[0006] In view of the shortcomings of the above-mentioned prior art, the purpose of the present invention is to provide a collaborative filtering recommendation method based on multiple similarities between users, so as to overcome the inability to accurately describe the differences between users for different item types by only using a single similarity in the traditional collaborative filtering recommendation algorithm. similarity of preferences

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