Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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Optimizing Similarity for User-based Collaborative Filtering

  • Soojung Lee (Dept. of Computer Education, Gyeongin National University of Education)
  • Received : 2024.10.04
  • Accepted : 2024.11.01
  • Published : 2024.11.29
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Abstract

Collaborative filtering is one of the most widely known implementation methods of recommender systems, which recommends items that similar users have preferred in the past. Therefore, similarity measurement is a very important factor that determines the performance of the system. In this study, in order to solve the shortcomings of the existing single or integrated heuristic similarity measures, the genetic algorithm was used to calculate the optimal similarity between users per item genre. In addition, in order to solve the data scalability problem, the number of users for calculating similarity for each genre was limited according to a preset threshold, and the average of the ratings of the items was used to solve the data sparsity problem. Through performance experiments, the optimal probabilities of the genetic operators were obtained and the prediction accuracy performance was analyzed. As a result, it was confirmed that the performance of the proposed method was superior to the existing methods, especially in a sparse data environment.

협력 필터링은 가장 널리 알려진 추천 시스템의 구현 방식들 중의 하나로서, 유사한 사용자들이 과거에 선호하였던 항목들을 추천한다. 따라서 유사도 측정은 시스템의 성능을 좌우하는 매우 중요한 요소이다. 본 연구에서는 기존의 단일의 또는 통합된 휴리스틱 유사도 척도의 단점을 해결하기 위한 목적으로, 유전 알고리즘을 활용하여 항목 장르별로 사용자 간 최적의 유사도를 산출하였다. 또한 데이터 확장성 문제를 해결하기 위하여 미리 설정한 임계치에 따라 각 장르별 유사도 산출 대상 사용자 수를 제한하였고, 데이터 희소성 문제 해결을 위하여 항목의 평가치 평균을 활용하였다. 성능 실험을 통하여 유전 연산자 확률의 적정값을 구하였고, 예측 성능을 분석한 결과, 제안 방법의 성능이 기존보다 우수하고, 특히 희소 데이터 환경에서 더욱 우수함을 확인하였다.

Keywords

References

  1. M. Jalili, S. Ahmadian, M. Izadi, P. Moradi, and M. Salehi, "Evaluating Collaborative Filtering Recommender Algorithms: A Survey," IEEE Access, Vol. 6, pp. 74003-74024, 2018. DOI: 10.1109/ACCESS.2018.2883742 
  2. B. Shao, X. Li, and G. Bian, "A Survey of Research Hotspots and Frontier Trends of Recommendation Systems from the Perspective of Knowledge Graph," Expert Systems with Applications, Vol. 165, 2021. DOI:10.1016/j.eswa.2020.113764 
  3. F. Fkih, "Similarity measures for Collaborative Filtering-based Recommender Systems: Review and Experimental Comparison," Journal of King Saud University - Computer and Information Sciences, Vol. 34, No. 9, pp. 7645-7669, 2022. DOI: 10.1016/j.jksuci.2021.09.014 
  4. B. Walek and P. Fajmon, "A Hybrid Recommender System for an Online Store using a Fuzzy Expert System," Expert Systems with Applications, Vol. 212, 2023. DOI:10.1016/j.eswa.2022.118565 
  5. H. Zhou, F. Xiong, and H. Chen, "A Comprehensive Survey of Recommender Systems Based on Deep Learning," Applied Sciences, Vol. 13, No. 20, 2023. DOI:10.3390/app132011378 
  6. R. Chen, Q. Hua, Y. -S. Chang, B. Wang, L. Zhang and X. Kong, "A Survey of Collaborative Filtering-based Recommender Systems: From Traditional Methods to Hybrid Methods based on Social Networks," IEEE Access, Vol. 6, pp. 64301-64320, 2018. DOI: 10.1109/ACCESS.2018.2877208 
  7. H. Khojamli and J. Razmara, "Survey of Similarity Functions on Neighborhood-based Collaborative Filtering," Expert Systems with Applications, Vol. 185, 2021, Article Number 115482, DOI: 10.1016/j.eswa.2021.115482
  8. A. A. Amer, and L. Nguyen, "Combinations of Jaccard with Numerical Measures for Collaborative Filtering Enhancement: Current Work and Future Proposal," ArXiv. /abs/2111.12202, 2021. DOI: 10.48550/arXiv.2111.12202 
  9. H.I. Abdalla, Y.A. Amer, L. Nguyen, A.A. Amer, and B.M. Al-Maqaleh, "Numerical Similarity Measures Versus Jaccard for Collaborative Filtering," Proceedings of the 9th Int'l Conf. Advanced Intelligent Systems and Informatics, 2023. DOI: 10.1007/978-3-031-43247-7_20 
  10. S. Bag, S.K. Kumar, and M.K. Tiwari, "An Efficient Recommendation Generation using Relevant Jaccard Similarity," Information Sciences, Vol. 483, pp. 53-64, 2019. DOI: 10.1016/j.ins.2019.01.023 
  11. B. Alhijawi and A. Awajan, "Genetic Algorithms: Theory, Genetic Operators, Solutions, and Applications," Evolutionary Intelligence, Vol. 17, pp. 1245-1256, 2024. DOI: 10.1007/s12065-023-00822-6 
  12. A. Livne, E. S. Tov, A. Solomon, A. Elyasaf, B. Shapira, and L. Rokach, "Evolving Context-aware Recommender Systems with Users in Mind," Expert Systems with Applications, Vol. 189, 2022, Article Number 116042, DOI: 10.1016/j.eswa.2021.116042 
  13. B. Alhijawi and Y. Kilani, "Using Genetic Algorithms for Measuring the Similarity Values between Users in Collaborative Filtering Recommender Systems," IEEE/ACIS 15th Int'l Conf. on Computer and Information Science, 2016. DOI: 10.1109/ICIS.2016.7550751 
  14. F.H. Nanehkaran, S.M. Lajevardi SM, and M.M. Bidgholi, "Nearest Neighbors Algorithm and Genetic-based Collaborative Filtering," Concurrency and Computation: Practice and Experience, Vol. 34, No. 1, 2022. DOI: 10.1002/cpe.6538 
  15. Y. Ar and E. Bostanci, "A Genetic Algorithm Solution to the Collaborative Filtering Problem," Expert Systems with Applications, Vol. 61, pp. 122-128, 2016. DOI: 10.1016/j.eswa.2016.05.021 
  16. B. Alhijawi and Y. Kilani, "A Collaborative Filtering Recommender System using Genetic Algorithm," Information Processing & Management, Vol. 57, No. 6, 2020. DOI: 10.1016/j.ipm.2020.102310 
  17. F.H. Nanehkaran, S.M. Lajevardi SM, and M.M. Bidgholi, "Optimization of Fuzzy Similarity by Genetic Algorithm in User-based Collaborative Filtering Recommender Systems," Expert Systems, Vol. 39, No. 4, 2022. DOI: 10.1111/exsy.12893 
  18. S. Lee, "Collaborative Filtering System Using Clustering and Genetic Algorithms," Communications in Computer and Information Science, Vol. 1071, 2019. DOI: 10.1007/978-981-32-9563-6_16 
  19. A. Laishram and V. Padmanabhan, "Discovery of User-item Subgroups via Genetic Algorithm for Effective Prediction of Ratings in Collaborative Filtering," Applied Intelligence, Vol. 49, pp. 3990-4006, 2019. DOI: 10.1007/s10489-019-01495-4 
  20. P.A. Khodke and P.B. Rathod, "Genetic Algorithm Based Similarity Transitivity in Collaborative Filtering," International Journal of Engineering Research and Technology, Vol. 2, No. 12, pp. 2933-2936, 2013. 
  21. B.S. Neysiani, N. Soltani, R. Mofidi, and M.H. Nadimi-Shahraki, "Improve Performance of Association Rule-Based Collaborative Filtering Recommendation Systems using Genetic Algorithm," Int'l J. of Information Technology and Computer Science, Vol. 11, No. 2, 2019. DOI: 10.5815/ijitcs.2019.02.06 
  22. Z. Liu, L. Wang, X. Li, and S. Pang, "A Multi-attribute Personalized Recommendation Method for Manufacturing Service Composition with Combining Collaborative Filtering and Genetic Algorithm," J. of Manufacturing Systems, Vol. 58, pp. 348-364, 2021. DOI: 10.1016/j.jmsy.2020.12.019 
  23. G. Koutrica, B. Bercovitz, and H. Garcia, "FlexRecs: Expressing and Combining Flexible Recommendations," Proc. the ACM SIGMOD International Conference on Management of Data, pp. 745-758, 2009. DOI: 10.1145/1559845.1559923