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

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Using Genre Rating Information for Similarity Estimation in Collaborative Filtering

  • Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
  • Received : 2019.09.17
  • Accepted : 2019.11.23
  • Published : 2019.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Similarity computation is very crucial to performance of memory-based collaborative filtering systems. These systems make use of user ratings to recommend products to customers in online commercial sites. For better recommendation, most similar users to the active user need to be selected for their references. There have been numerous similarity measures developed in literature, most of which suffer from data sparsity or cold start problems. This paper intends to extract preference information as much as possible from user ratings to compute more reliable similarity even in a sparse data condition, as compared to previous similarity measures. We propose a new similarity measure which relies not only on user ratings but also on movie genre information provided by the dataset. Performance experiments of the proposed measure and previous relevant measures are conducted to investigate their performance. As a result, it is found that the proposed measure yields better or comparable achievements in terms of major performance metrics.

유사도 계산은 메모리 기반 협력필터링 시스템의 성능에 매우 중요하다. 이 시스템들은 사용자 평가치들을 이용하여 온라인 상업 사이트에서 고객들에게 상품을 추천한다. 더욱 적합한 추천을 위해 현 사용자와 가장 유사한 사용자들을 선정하여 참조한다. 기존 문헌에는 많은 유사도 척도들이 개발되었는데, 이들은 대개 데이터 희소성이나 완전 시작 문제를 내포하고 있다. 본 논문에서는 기존 척도들과는 달리 사용자 평가치들로부터 선호 정보를 최대한 추출함으로써 희소한 데이터 조건에서도 더욱 신뢰할 수 있는 유사도값을 산출하고자 한다. 사용자 평가치 뿐만 아니라 데이터셋이 제공하는 영화장르 정보를 이용하는 새로운 유사도 척도를 제시한다. 본 척도와 기존의 관련된 척도들의 성능 실험을 하였고, 그 결과, 제안 척도는 주요 성능 평가기준 상으로 더욱 우수하거나 유사한 성능 결과를 보임을 확인하였다.

Keywords

References

  1. X. Su and T.M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, 2009. DOI:10.1155/2009/421425
  2. S. Du, H. Zhang, H. Xu, J. Yang, and O. Tu, "To Make the Travel Healthier: A New Tourism Personalized Route Recommendation Algorithm," Journal of Ambient Intelligence and Humanized Computing, Vol. 10, No. 9, pp. 3551-3562, 2019. DOI:10.1007/s12652-018-1081-z
  3. J. Gupta and J. Gadge, "Performance Analysis of Recommendation System based on Collaborative Filtering and Demographics," International Conference on Communication Information & Computing Technology, pp. 1-6, 2015. DOI: 10.1109/ICCICT.2015.7045675
  4. M. Aamir and M. Bhusry, “Recommendation System: State of the Art Approach,” International Journal Computer Applications, Vol. 120, No. 12, pp. 25-32, 2015. DOI: 10.5120/21281-4200
  5. 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
  6. K.G. Saranya, G.S. Sadasivam, and M. Chandralekha, "Performance Comparison of Different Similarity Measures for Collaborative Filtering Technique," Indian Journal of Science and Technology, Vol. 9, No. 29, 2016. DOI: 10.17485/ijst/2016/v9i29/91060
  7. Z. Y. Hafshejani, M. Kaedi, and A. Fatemi, “Improving Sparsity and New User Problems in Collaborative Filtering by Clustering the Personality Factors,” Electronic Commerce Research, Vol. 18, No. 4, pp. 813-836, 2018. DOI: 10.1007/s10660-018-9287-x
  8. Koohi and K. Kiani, "A New Method to Find Neighbor Users that Improves the Performance of Collaborative Filtering," Expert Systems With Applications, Vol. 83, pp. 30-39, 2017. DOI: 10.1016/j.eswa.2017.04.027
  9. B. Zhu, R. Hurtado, J. Bobadilla, and F. Ortega, "An Efficient Recommender System Method based on the Numerical Relevances and the Non-numerical Structures of the Ratings," IEEE Access, Vol. 6, pp. 49935-49954, 2018. DOI: 10.1109/ACCESS.2018.2868464
  10. M. Li and K. Zheng, "A Collaborative Filtering Algorithm Combined with User Habits for Rating," International Conference on Logistics Engineering, Management and Computer Science, pp 1279-1282, 2015. DOI: 10.2991/lemcs-15.2015.255
  11. W. Wang, G. Zhang, and J. Lu, “Collaborative Filtering with Entropy-driven User Similarity in Recommender Systems,” International Journal of Intelligent Systems, Vol. 30, No. 8, pp. 854-870, 2015. DOI: 10.1002/int.21735
  12. S. Lee, "Using Entropy for Similarity Measures in Collaborative Filtering," Journal of Ambient Intelligence and Humanized Computing, Feb. 2019. DOI: 10.1007/s12652-019-01226-0
  13. G. Koutrica, B. Bercovitz, and H. Garcia, "FlexRecs: Expressing and Combining Flexible Recommendations," Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, pp. 745-758, 2009.
  14. J. Bobadilla, F. Serradilla, and J. Bernal, “A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems,” Knowledge Based Systems, Vol. 23, No. 6, pp. 520-528, 2010. DOI: 10.1016/j.knosys.2010.03.009
  15. S. Lee, "Improving Jaccard Index for Measuring Similarity in Collaborative Filtering," Lecture Notes in Electrical Engineering, Vol. 424, pp. 799-806, 2017. DOI: 10.1007/978-981-10-4154-9_93
  16. M. Salehi, I. N. Kamalabadi, and M. B. Ghaznavi-Ghoushchi, "Attribute-based Collaborative Filtering using Genetic Algorithm and Weighted C-means Algorithm," International Journal of Business Information Systems, Vol. 13, No. 3, pp. 265-283, 2013. DOI: 10.1504/IJBIS.2013.054465
  17. F. Cacheda, V. Carneiro, D. Fernandez, and V. Formoso, “Comparison of Collaborative Filtering Algorithms: Limitations of Current Techniques and Proposals for Scalable, High-performance Recommender Systems,” ACM Transactions on the Web, Vol. 5, No. 1, pp. 1-33, 2011. DOI: 10.1145/1921591.1921593