THE IMPROVED MODEL OF USER SIMILARITY COEFFICIENTS COMPUTATION FOR RECOMMENDATION SYSTEMS

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DOI: https://doi.org/10.20998/2522-9052.2020.3.06
Keywords:
recommendation systems, similarity coefficients, collaborative filtering, data analysis, optimization

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Yelyzaveta Meleshko
Central Ukrainian National Technical University, Kropyvnytskyi
https://orcid.org/0000-0001-8791-0063
Oleksandr Drieiev
Central Ukrainian National Technical University, Kropyvnytskyi
https://orcid.org/0000-0001-6951-2002
Anas Mahmoud Al-Oraiqat
Onaizah University, Onaizah
https://orcid.org/0000-0002-1071-6331

Abstract

The subject matter of the article is a model of calculating the user similarity coefficients of the recommendation systems. The urgency of the development is determined by the need to improve the quality of recommendation systems by adapting the time characteristics to possible changes in the similarity coefficients of users. The goal is the development of the improved model of user similarity coefficients calculation for recommendation systems to optimize the time of forming recommendation lists. The tasks to be solved are: to investigate the probability of changing user preferences of a recommendation system by comparing their similarity coefficients in time, to investigate which distribution function describes the changes of similarity coefficients of users in time. The methods used are: graph theory, probability theory, radioactivity theory, algorithm theory. Conclusions. In the course of the researches, the model of user similarity coefficients calculating for the recommendation systems has been improved. The model differs from the known ones in that it takes into account the recalculation period of similarity coefficients for the individual user and average recalculation period of similarity coefficients for all users of the system or a specific group of users. The software has been developed, in which a series of experiments was conducted to test the effectiveness of the developed method. The conducted experiments showed that the developed method in general increases the quality of the recommendation system without significant fluctuations of Precision and Recall of the system. Precision and Recall can decrease slightly or increase, depending on the characteristics of the incoming data set. The use of the proposed solutions will increase the application period of the previously calculated similarity coefficients of users for the prediction of preferences without their recalculation and, accordingly, it will shorten the time of formation and issuance of recommendation lists up to 2 times.

Article Details

How to Cite
Meleshko, Y., Drieiev, O., & Al-Oraiqat, A. M. (2020). THE IMPROVED MODEL OF USER SIMILARITY COEFFICIENTS COMPUTATION FOR RECOMMENDATION SYSTEMS. Advanced Information Systems, 4(3), 52–61. https://doi.org/10.20998/2522-9052.2020.3.06
Issue
Vol. 4 No. 3 (2020): Advanced Information Systems
Section
Information systems modeling
Author Biographies

Yelyzaveta Meleshko, Central Ukrainian National Technical University, Kropyvnytskyi

Candidate of Technical Sciences, Associate Professor, Doctoral Student of Cybersecurity and Software Department

Oleksandr Drieiev, Central Ukrainian National Technical University, Kropyvnytskyi

Candidate of Technical Sciences, Associate Professor of Cybersecurity and Software Department

Anas Mahmoud Al-Oraiqat, Onaizah University, Onaizah

Candidate of Technical Sciences, a staff member at the Computer and Information Sciences Dept.

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