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

  • 제24권9호
  • /
  • Pages.77-82
  • /
  • 2019
  • /
  • 1598-849X(pISSN)
  • /
  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
권호 표지
DOI QR코드

DOI QR Code

Using User Rating Patterns for Selecting Neighbors in Collaborative Filtering

  • Lee, Soojung (Dept. of Computer Education, Gyeongin National University of Education)
  • 투고 : 2019.07.29
  • 심사 : 2019.09.03
  • 발행 : 2019.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

키워드

참고문헌

  1. 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. https://doi.org/10.1007/s12652-018-1081-z
  2. X. Su and T.M. Khoshgoftaar, "A Survey of Collaborative Filtering Techniques," Advances in Artificial Intelligence, 2009.
  3. M. Aamir and M. Bhusry, “Recommendation System: State of the Art Approach,” International Journal Computer Applications, Vol. 120, No. 12, pp. 25-32, 2015. https://doi.org/10.5120/21281-4200
  4. U. A. Anusha and S. Biradar, “Recommender Systems: A Survey,” International Journal of Latest Technology in Engineering, Management & Applied Science, Vol. V, No. I, pp. 42-45, 2016.
  5. R. Prasad and V. V. Kumari, "A Categorical Review of Recommender Systems," Inernational Journal of Distributed and Parallel Systems, Vol. 3, No. 5, pp. 73-84. 2012. https://doi.org/10.5121/ijdps.2012.3507
  6. K. Madadipouya and S. Chelliah, “A Literature Review on Recommender Systems Algorithms, Techniques and Evaluations,” BRAIN: Broad Research in Artificial Intelligence and Neuroscience, Vol. 8, No. 2, pp. 109-124, 2017.
  7. 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.
  8. 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.
  9. J. Bobadilla, F. Ortega, and A. Hernando, “A Collaborative Filtering Similarity Measure based on Singularities,” Information Processing and Management, Vol. 48, No. 2, pp. 204-217, 2012. https://doi.org/10.1016/j.ipm.2011.03.007
  10. H. Liu, Z. Hu, A. Mian, H. Tian, and X. Zhu, "A New User Similarity Model to Improve the Accuracy of Collaborative Filtering," Knowledge-Based Systems, Vol. 56, pp. 156-166, 2014. https://doi.org/10.1016/j.knosys.2013.11.006
  11. C.C. Chen, Y.H. Wan, M.C. Chung, and Y.C. Sun, "An Effective Recommendation Method for Cold Start New Users using Trust and Distrust Networks," Information Sciences, Vol. 224, pp. 19-36, 2013. https://doi.org/10.1016/j.ins.2012.10.037
  12. 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.
  13. 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. https://doi.org/10.1016/j.knosys.2010.03.009
  14. 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. https://doi.org/10.1109/ACCESS.2018.2868464
  15. S. Lee, "Improving Jaccard Index for Measuring Similarity in Collaborative Filtering," Lecture Notes in Electrical Engineering, Vol. 424, pp. 799-806, 2017. https://doi.org/10.1007/978-981-10-4154-9_93
  16. 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.
  17. W. Wang, G. Zhang, and J. Lu, “Collaborative Filtering with Entropy-driven User Similarity in Recommender Systems,” Interational Journal of Intelligent Systems, Vol. 30, No. 8, pp. 854-870, 2015. https://doi.org/10.1002/int.21735
  18. H.-J. Kwon, T.-H. Lee, J.-H. Kim, and K.-S. Hong, "Improving Prediction Accuracy using Entropy Weighting in Collaborative Filtering," Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, pp. 40-45, 2009.
  19. S. Lee, "Using Entropy for Similarity Measures in Collaborative Filtering," Journal of Ambient Intelligence and Humanized Computing, Feb. 2019.
  20. L. Baltrunas, T. Makcinskas, and F. Ricci, "Group Recommendation with Rank Aggregation and Collaborative Filtering," Proceedings of the ACM Conference on Recommender Systems, pp 119-126, 2010.