METHOD OF COLLABORATIVE FILTRATION BASED ON ASSOCIATIVE NETWORKS OF USERS SIMILARITY

Main Article Content

Yelyzaveta Meleshko
https://orcid.org/0000-0001-8791-0063

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.

Article Details

How to Cite
Meleshko, Y. (2018). METHOD OF COLLABORATIVE FILTRATION BASED ON ASSOCIATIVE NETWORKS OF USERS SIMILARITY. Advanced Information Systems, 2(4), 55–59. https://doi.org/10.20998/2522-9052.2018.4.09
Section
Methods of information systems synthesis
Author Biography

Yelyzaveta Meleshko, Central Ukrainian National Technical University, Kropyvnytsky

Candidate of Technical Sciences, Associate Professor, Doctoral Candidate of the Department Cyber Security and Software Engineering

References

Meleshko Е.V., Semenov, S.G. and Khokh, V.D. (2018), “Research of methods of building advisory systems on the internet”, Academic Journal "Control, Navigation and Communication Systems”, Issue 1(47), Poltava National Technical Yuri Kon-dratyuk University, Poltava, pp. 131–136, DOI: https://doi.org/10.26906/SUNZ.2018.1.131 (in Ukrainian).

Jones, M. (2013), Recommender systems, Part 1. Introduction to approaches and algorithms. Learn about the concepts that un-derlie web recommendation engines, available at:

https://www.ibm.com/developerworks/opensource/library/os-recommender1/index.html?s_tact=105agx99&s_cmp=cp

Shahidi, A. (2015), Introduction to analysis of the associative rules, available at:

https://basegroup.ru/community/articles/intro (in Russian).

Agrawal, R. and Srikant, R. (1994), “Fast Discovery of Association Rules”, Proc. of the 20th International Conference on VLDB, Santiago, Chile, 1215, pp. 487-499.

Savasere, A., Omiecinski, E. and Navathe S. (1995), “An Efficient Algorithm for Mining Association Rules in Large Data-bases”, Proc. 21st Int’l Conf. Very Large Data Bases, Morgan Kaufmann, San Francisco, pp.422-434.

Park, J.S., Chen, M.-S. and Philip, S.Y. (1995), “An Effective HashBased Algorithm for Mining Association Rules”, Proc. ACM SIGMOD Int’l Conf. Management of Data, ACM Press, New York, pp. 175-186.

Brin, S., Motwani, R., Ullman, Jeffrey D. and Tsur, S. (1997) “Dynamic Itemset Counting and Implication Rules for Market Basket Data”, Proc. ACM SIGMOD Int’l Conf. Management of Data, ACM Press, New York, pp. 255-264

(2018), Neo4j Documentation , available at: https://neo4j.com/docs/

Harper, F.M. and Konstan J.A. (2016) “The MovieLens Datasets: History and Context”, ACM Transactions on Interactive In-telligent Systems (TiiS), No. 19 , DOIt: https://doi.org/10.1145/2827872.