METHOD OF COLLABORATIVE FILTRATION BASED ON ASSOCIATIVE NETWORKS OF USERS SIMILARITY

Abstract

The subject matter of the article is the processes of generating a recommendations list for users of a website. The goal is to develop the new method of collaborative filtering based on building associative networks of users similarity to improve the quality of recommender systems. The tasks to be solved are: to develop the method of collaborative filtering based on building associative networks of user similarity, develop software to test this method, conduct experiments on the developed software to test the effectiveness of the developed method, determine the quality of its work and compare this method with the standard method of collaborative filtering. The methods used are: graph theory, mathematical statistics, the theory of algorithms, object-oriented programming. The following results were obtained: the method of collaborative filtering based on building associative networks of user similarity was developed, to implement this method the software was developed, experiments using the developed software to test the developed method were conducted. Conclusions. The possibility of using associative networks in recommender systems was researched. The associative rule for building associative networks of users similarity was proposed. The collaborative filtering method based on associative networks of users similarity, which can be used to improve the quality of recommender systems, was developed. Experiments conducted on the developed software have shown that the developed method significantly increases such performance indicators of the recommender system as user space coverage, item space coverage, user interaction coverage, and makes it possible to create better-quality lists of recommendations for website users.

Keywords

• recommendation systems
• collaborative filtering
• associative networks
• similarity coefficients