Journal of Intelligence and Information Systems (지능정보연구)

Korea Intelligent Information System Society (한국지능정보시스템학회)
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New Collaborative Filtering Based on Similarity Integration and Temporal Information

통합유사도 함수의 이용과 시간정보를 고려한 협업필터링 기반의 추천시스템

  • Choi, Keun-Ho (Business School, Korea University) ;
  • Kim, Gun-Woo (Department of Business Administration, Hanbat National University) ;
  • Yoo, Dong-Hee (Department of Electronics Engineering and Information Science, Korea Military Academy) ;
  • Suh, Yong-Moo (Business School, Korea University)
  • 최근호 (고려대학교 경영대학 경영학과) ;
  • 김건우 (한밭대학교 경상대학 경영학과) ;
  • 유동희 (육군사관학교 전자정보학과) ;
  • 서용무 (고려대학교 경영대학 경영학과)
  • Received : 2011.07.27
  • Accepted : 2011.08.16
  • Published : 2011.09.30
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Abstract

As personalized recommendation of products and services is rapidly growing in importance, a number of studies provided fundamental knowledge and techniques for developing recommendation systems. Among them, the CF technique has been most widely used and has proven to be useful in many practices. However, current collaborative filtering (CF) technique has still considerable rooms for improving the effectiveness of recommendation systems: 1) a similarity function most systems use to find so-called like-minded people is not well defined in that similarity is computed from a single perspective of similarity concept; and 2) temporal information that contains the changing preference of customers needs to be taken into account when making recommendations. We hypothesize that integration of multiple aspects of similarity and utilization of temporal information will improve the accuracy of recommendations. The objective of this paper is to test the hypothesis through a series of experiments using MovieLens data. The experimental results show that the proposed recommendation system highly outperforms the conventional CF-based systems, confirming our hypothesis.

상품 및 서비스에 대한 개인화된 추천 서비스가 중요해짐에 따라, 많은 연구자들은 추천시스템 개발을 위한 다양한 지식과 기법들을 제공해왔다. 이러한 기법들 중에서 협업 필터링(Collaborative Fitering) 기법은 여러 분야에서 널리 사용되고 있으며, 그 유용성이 입증되었다. 하지만, 추천시스템의 성능을 더욱 높이기 위해서 현재의 협업 필터링 기법은 다음과 같은 점들을 고려해야 한다. 첫째, 대부분의 추천시스템과 관련한 연구에서 특정 고객과 성향이 유사한 다른 고객들을 찾기 위해 사용되는 유사도 함수들(Similarity Functions)은 대부분 특정한 관점에 초점을 두고 있기 때문에 다양한 관점에서 성향이 유사한 다른 고객들을 찾는데 한계를 가진다. 따라서, 특정 관점에 치우치지 않는 통합된 유사도 함수를 사용해야 할 필요가 있다. 둘째, 고객들의 성향은 시간이 지남에 따라 변화하기 때문에, 이를 추천결과에 반영하기 위해서는 시간에 따른 고객들의 구매 성향의 변화를 고려해야 한다. 본 연구는 여러 실험들을 통해 다음의 가설을 검정하는 것을 목적으로 하였다-다양한 관점이 동시에 반영된 통합 유사도 함수의 이용과 시간정보를 이용한 사용자의 구매 성향의 변화를 반영할 경우 추천의 정확도가 향상될 것이다. 다양한 실험을 통해, 본 연구에서 제시한 추천시스템은 전통적인 협업 필터링 기반의 추천시스템들에 비해 일반적으로 상당히 높은 정확도를 보였으며 이를 통해, 본 연구에서 제시한 가설이 채택될 수 있음을 확인하였다.

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References

  1. Adomavicius, G., "Towards the Next Generation of Recommender Systems: A Survey of the State‐of‐the‐Art and Possible Extensions", IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6(2005), 734-749.
  2. Adomavicius, G. and Y. Kwon, "New Recommendation Techniques for Multicriteria Rating Systems", IEEE Intelligent Systems, Vol.22, No.3(2007), 48-55. https://doi.org/10.1109/MIS.2007.58
  3. Aggarwal, C. C., C. Procopiuc and P. S. Yu, "Finding Localized Associations in Market Basket Data", IEEE Transactions on Knowledge and Data Engineering, Vol.14, No.1 (2002), 51-62. https://doi.org/10.1109/69.979972
  4. Ahn, H. J., "A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold‐Starting Problem", Information Sciences, Vol.178, No.1(2008), 37-51. https://doi.org/10.1016/j.ins.2007.07.024
  5. Albadvi, A. and M. Shahbazi, "A Hybrid Recommendation Technique based on Product Category Attributes", Expert Systems with Applications, Vol.36, No.9(2009), 11480-11488. https://doi.org/10.1016/j.eswa.2009.03.046
  6. Baeza‐Yates, R. A. and B. Ribeiro‐Neto, Modern Information Retrieval, Addison‐Wesley Longman Publishing Co., Inc., Boston, 1999.
  7. Balabanovic, M. and Y. Shoham, "Content‐Based, Collaborative Recommendation", Communications of the ACM, Vol.40, No.3(1998), 66-72.
  8. Belkin, N. J. and W. B. Croft, "Information Filtering and Information Retrieval: Two Sides of the Same Coin", Communications of the ACM, Vol.35, No.12(1992), 29-38. https://doi.org/10.1145/138859.138861
  9. Billsus, D. and M. J. Pazzani, "Learning Collaborative Information Filters", 15th International Conference on Machine Learning, (1998), 48-56
  10. Billsus, D. and M. J. Pazzani, "User Modeling for Adaptive News Access", User Modeling and User‐Adapted Interaction, Vol.10, No.2 (2000), 147-180. https://doi.org/10.1023/A:1026501525781
  11. Bobadilla, J., F. Serradilla and J. Bernal, "A New Collaborative Filtering Metric that Improves the Behavior of Recommender Systems", Knowledge‐Based Systems, Vol.23, No.6(2010), 520-528. https://doi.org/10.1016/j.knosys.2010.03.009
  12. Chen, L. S., F. H. Hsu, M. C. Chen and Y. C. Hsu, "Developing Recommender Systems with the Consideration of Product Profitability for Sellers", Information Sciences, Vol.178, No.4(2008), 1032-1048. https://doi.org/10.1016/j.ins.2007.09.027
  13. Cheung, K. W., J. T. Kwok, M. H. Law, and K. C. Tsui, "Mining Customer Product Ratings for Personalized Marketing", Decision Support Systems, Vol.35, No.2(2003), 231-243. https://doi.org/10.1016/S0167-9236(02)00108-2
  14. Getoor, L. and M. Sahami, "Using Probabilistic Relational Models for Collaborative Filtering", Workshop on Web Usage Analysis and User Profiling, (1999), 1-6.
  15. Goldberg, D., D. Nichols, B. M. Oki and D. Terry, "Using Collabortive Filtering to Weave an Information Tapestry", Communications of the ACM, Vol.35, No.12(1992), 61-70. https://doi.org/10.1145/138859.138867
  16. Goldberg, K., T. Roeder, D. Gupta and C. Perkins, "Eigentaste: A Constant Time Collaborative Filtering Algorithm", Information Retrieval, Vol.4, No.2(2001), 133-151. https://doi.org/10.1023/A:1011419012209
  17. Hofmann, T., "Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis", 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, (2002), 259-266.
  18. Hofmann, T., "Latent Semantic Models for Collaborative Filtering", ACM Transactions on Information Systems, Vol.22, No.1(2004), 89-115. https://doi.org/10.1145/963770.963774
  19. Huang, C. L. and W. L. Huang, "Handling Sequential Pattern Decay: Developing a Two Stage Collaborative Recommendation System", Electronic Commerce Research and Applications, Vol.8, No.3(2009), 117-129. https://doi.org/10.1016/j.elerap.2008.10.001
  20. Jeong, B., J. Lee and H. Cho, "An Iterative Semi‐Explicit Rating Method for Building Collaborative Recommender Systems", Expert Systems with Applications, Vol.36, No.3 (2009), 6181-6186. https://doi.org/10.1016/j.eswa.2008.07.085
  21. Joaquin, D. and I. Naohiro, "Memory‐based Weighted‐Majority Prediction", ACM SIGIR ʼ99 Workshop on Recommender Systems: Algorithms and Evaluation, (1999), 1-5.
  22. Kim, H. N., A. T. Ji, I. Ha and G. S. Jo, "Collaborative Filtering based on Collaborative Tagging for Enhancing the Quality of Recommendation", Electronic Commerce Research and Applications, Vol.9, No.1(2010), 73-83. https://doi.org/10.1016/j.elerap.2009.08.004
  23. Kim, H. K., J. K. Kim and Y. U. Ryu, "Personalized Recommendation over a Customer Network for Ubiquitous Shopping", IEEE Transactions on Services Computing, Vol.2, No.2(2009), 140-151. https://doi.org/10.1109/TSC.2009.7
  24. Kim, J. K., H. K. Kim and Y. H. Cho, "A User‐Oriented Contents Recommendation System in Peer‐to‐Peer Architecture", Expert Systems with Applications, Vol.34, No.1(2008), 300-312. https://doi.org/10.1016/j.eswa.2006.09.034
  25. Kumar, R., P. Raghavan, S. Rajagopalan and A. Tomkins, "Recommender Systems: A Probabilistic Analysis", Journal of Computer and System Science, Vol.63, No.1(2001), 42-61. https://doi.org/10.1006/jcss.2001.1757
  26. Kwon, K., J. Cho and Y. Park, "Multidimensional Credibility Model for Neighbor Selection in Collaborative Recommendation", Expert Systems with Applications, Vol.36, No.3 (2009), 7114-7122. https://doi.org/10.1016/j.eswa.2008.08.071
  27. Lang, K., "NewsWeeder: Learning to Filter Netnews", 12th International Conference on Machine Learning, (1995), 1-9.
  28. Lee, J. S. and S. Olafsson, "Two‐way Cooperative Prediction for Collaborative Filtering Recommendations", Expert Systems with Applications, Vol.36, No.3(2009), 5353-5361. https://doi.org/10.1016/j.eswa.2008.06.106
  29. Lee, T. Q., Y. Park and Y. T. Park, "A Timebased Approach to Effective Recommender Systems Using Implicit Feedback", Expert Systems with Applications, Vol.34, No.4 (2008), 3055-3062. https://doi.org/10.1016/j.eswa.2007.06.031
  30. Liu, D. R., C. H. Lai and W. J. Lee, "A Hybrid of Sequential Rules and Collaborative Filtering for Product Recommendation", Information Sciences, Vol.179, No.20(2009), 3505-3519. https://doi.org/10.1016/j.ins.2009.06.004
  31. Liu Z., W. Qu, H. Li and C. Xie, "A Hybrid Collaborative Filtering Recommendation Mechanism for P2P Networks", Future Generation Computer Systems, Vol.26, No.8(2010), 1409-1417. https://doi.org/10.1016/j.future.2010.04.002
  32. Marlin, B., "Modeling User Rating Profiles for Collaborative Filtering", Advances in Neural Information Processing Systems, Vol.16(2003), 627-634.
  33. Mooney, R. J. and L. Roy, "Content‐Based Book Recommending Using Learning for Text Categorization", ACM SIGIR ʻ 99 Workshop on Recommender Systems: Algorithms and Evaluation, (1999), 1-8.
  34. Nakamura, A. and N. Abe, "Collaborative Filtering using Weighted Majority Prediction Algorithm", 15th International Conference on Machine Learning, (1998), 395-403.
  35. Park, Y. J. and K. N. Chang, "Individual and Group Behavior‐based Customer Profile Model for Personalized Product Recommendation", Expert Systems with Applications, Vol.36, No.2(2009), 1932-1939. https://doi.org/10.1016/j.eswa.2007.12.034
  36. Pavlov, D. Y. and D. M. Pennock, "A Maximum Entropy Approach to Collaborative Filtering in Dynamic, Sparse, High‐Dimensional Domains", Advances in Neural Information Processing Systems, Vol.15(2002), 1441-1448.
  37. Pazzani, M. and D. Billsus, "Learning and Revising User Profile: The Identification of Interesting Web Sites", Machine Learning, Vol.27, No.3(1997), 313-331. https://doi.org/10.1023/A:1007369909943
  38. Pennock, D. M., E. Horvitz, S. Lawrence and C. L. Giles, "Collaborative Filtering by Personality Diagnosis: A Hybrid Memory‐and Model‐ Based Approach", 16th Conference on Uncertainty in Artificial Intelligence, (1999), 473-480.
  39. Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom and J. Riedl, "GroupLens: An Open Architecture for Collaborative Filtering of Netnews", ACM Conference on Computer Supported Cooperative Work, (1994), 175-186.
  40. Russell, S. and V. Yoon, "Applications of Wavelet Data Reduction in a Recommender System", Expert Systems with Applications, Vol. 34, No.4(2008), 2316-2325. https://doi.org/10.1016/j.eswa.2007.03.009
  41. Salter, J. and N. Antonopoulos, "Cinema Screen Recommender Agent: Combining Collaborative and Content‐Based Filtering", IEEE Intelligent Systems, Vol.21, No.1(2006), 35-41. https://doi.org/10.1109/MIS.2006.4
  42. Salton, G., Automatic Text Processing, Addison‐Wesley Longman Publishing Co., Inc., Boston, 1988.
  43. Shani, G., D. Heckerman, and R. I. Brafman, "An MDP‐based Recommender System", Journal of Machine Learning Research, Vol.6, No.2 (2002), 1265-1295.
  44. Shardanand, U. and P. Maes, "Social Information Filtering Algorithms for Automating 'Word of Mouth'", SIGCHI Conference on Human Factors in Computing Systems, (1995), 210-217.
  45. Si, L. and R. Jin, "Flexible Mixture Model for Collaborative Filtering", 20th International Conference on Machine Learning, (2003), 1-8.
  46. Symeonidis, P., A. Nanopoulos and Y. Manolopoulos, "Providing Justifications in Recommender Systems", IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems an Humans, Vol.38, No.6(2008), 1262-1272. https://doi.org/10.1109/TSMCA.2008.2003969
  47. Tang, T. Y., P. Winoto and K. C. C. Chan, "Scaling Down Candidate Sets based on the Temporal Feature of Items for Improved Hybrid Recommendations", Intelligent Techniques for Web Personalization, (2005), 169-186.
  48. Wang, Y., W. Dai and Y. Yuan, "Website Browsing Aid: A Navigation Graph‐based Recommendation System", Decision Support Systems, Vol.45, No.3(2008), 387-400. https://doi.org/10.1016/j.dss.2007.05.006
  49. Wei, C. P., C. S. Yang and H. W. Hsiao, "A Collaborative Filtering‐based Approach to Personalized Document Clustering", Decision Support Systems, Vol.45, No.3(2008), 413-428. https://doi.org/10.1016/j.dss.2007.05.008
  50. Yu, K., A. Schwaighofer, V. Tresp, X. Xu and H. P. Kriegel, "Probabilistic Memory‐based Collaborative Filtering", IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No.1(2004), 56-69. https://doi.org/10.1109/TKDE.2004.1264822
  51. Yu, K., X. Xu, J. Tao, M. Ester, and H. P. Kriegel, "Instance Selection Techniques for Memory‐based Collaborative Filtering", 2nd SIAM International Conference on Data Mining, (2002), 1-16.

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