The Journal of Society for e-Business Studies (한국전자거래학회지)

Society for e-Business Studies (한국전자거래학회)
권호 표지

Collaborative Filtering by Consistency Based Trust Definition

일관성 기반의 신뢰도 정의에 의한 협업 필터링

  • 김형도 (한양사이버대학교 경영학부)
  • Published : 2009.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Many neighbors are needed for making the recommendation quality better and stable in collaborative filtering. Furthermore, the quality is not so good mainly due to a reason that high similarity between two users does not guarantee the same preference to items considered for recommendation. Dissimilar users who have consistency in item selection can be useful for predicting preferences. This paper proposes a new collaborative filtering method, defining trust based on consistency for improving this phenomenon. Empirical studies show that such a method reduces the number of neighbors required to make the recommendation quality stable and the recommendation quality itself is also significantly improved.

사용자간 유사도에 의한 협업 필터링에서 추천 품질이 안정적인 상태에 이르기 위해서는 많은 이웃들이 필요하다. 이것은 높은 사용자간 유사도가 제품에 대한 동일한 선호도를 일관되게 보장하지 못하기 때문이다. 유사하지 않은 사용자라 할지라도 제품 선택에서 사용자 간에 일관성이 있다면, 선호도 예측에서 유용하게 사용될 수 있다. 이 논문에서는 일관성을 기준으로 신뢰도를 정의하고, 이를 기반으로 이웃을 선정하여 선호도를 예측하는 협업 필터링 방법을 제시한다. 이 방법에 의한 추천 품질이 안정적인 상태에 이르기 위해서 필요한 이웃들의 수가 유사도에 의한 방법보다 매우 적으며, 추천 품질 또한 우수하다.

Keywords

References

  1. 박지수, 정석재, 이영훈, 김경섭, “퍼지추론을 통한 규칙 기반의 보험상품 추천 및 설계 시스템 구현”, 한국전자거래학회지, 제12권, 제1호, pp. 99-122, 2007.
  2. 이재식, 명훈식, “사례기반 추론을 이용한 인터넷 서점의 서적 추천시스템 개발”, 한국전자거래학회지, 제13권, 제4호,pp. 173-191, 2008.
  3. Adomavicius, G. et al., “Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach,” ACM Transactions on Information Systems, Vol. 23, No. 1, pp. 103-145, Jan. 2005. https://doi.org/10.1145/1055709.1055714
  4. Ahn, H. J., “A New Similarity Measure for Collaborative Filtering to Alleviate the New User Cold-Starting Problem,” Information Sciences, Vol. 128, pp. 37-51, 2008.
  5. Chen, A., “Context-Aware Collaborative Filtering System : Predicting the User’s Preferences in Ubiquitous Computing,” Proceedings of CHI 2005, Portland, USA, Apr. pp. 2-7, 2005.
  6. GroupLens Research, “MovieLens Datasets,” http://www.grouplens.org/taxonomy/term/14
  7. Herlocker, J. L. et al., “An Algorithmic Framework for Performing Collaborative Filtering,” Proceedings of the 22nd Annual International ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 230-237, Berkeley,USA, 1999.
  8. Herlocker, J. L. et al., “Evaluating Collaborative Filtering Recommender Systems,” ACM Transactions on Information Systems, Vol. 22, No. 1, pp. 5-53, 2004. https://doi.org/10.1145/963770.963772
  9. Kim, H.-N. et al., “Error-Based Collaboration Filtering Algorithm for Top-N Recommendation,” Proceedings of APWeb 2007 and WAIM 2007(LNCS 4505), pp. 594-605, Huang Shan, China, June, pp.16-18, 2007.
  10. Kim, T.-H., and Yang, S.-B., “An Improved Neighbor Selection Algorithm in Collaborative Filtering,” IEICE Transactions on Information and Systems, Vol. E88-D, No. 5, pp. 1072-1076, 2005. https://doi.org/10.1093/ietisy/e88-d.5.1072
  11. Konstan, J. et al., “GroupLens : Applying Collaborative Filtering to Usenet News,” Communications of the ACM, Vol. 40, No. 3, pp. 77-87, 1997. https://doi.org/10.1145/245108.245126
  12. Lee, H. C., Lee, S. J., and Chung Y. J., “A Study on the Improved Collaborative Filtering Algorithm for Recommender System,” Proceedings of the 5th International Conference on Software Engineering Research, Management and Applications (SERA2007), pp. 297-304, 2007.
  13. Lemire, D., and Maclachlan, A., “Slope One Predictors for Online Rating-Based Collaborative Filtering,” Proceedings of the SDM’05, Newport Beach, USA, Apr, pp. 21-23, 2005.
  14. Massa, P., and Avesani, P., “Trust-Aware Collaborative Filtering for Recommender Systems,” Proceedings of the International Conf. on Cooperative Information Systems 2004.
  15. Mohan, B. K., Keller, B. J., and Ramakrishnan, N., “Scouts, Promoters, and Connectors : The Roles of Ratings in Nearest Neighbor Collaborative Filtering,” Proceedings of EC’06, pp. 250-259, Ann Arbor, Michigan, USA, Jun, pp. 11-15, 2006.
  16. Nakamoto, R. et al., “Tag-Based Contextual Collaborative Filtering,” Proceedings of DEWS2007, pp. 25-30, Hiroshima, Japan, 2007.
  17. Papagelis, M, Plexousakis, D., and Kutsuras, T., “Alleviating the Sparsity Problem of Collaborative Filtering Using Trust Inference,” Proceedings of iTrust 2005, pp. 224-239, 2005.
  18. Sarwar B. et al., “Analysis of Recommendation Algorithms for e-Commerce,” Proceedings of the 2nd ACM Conf. on Electronic Commerce, pp. 158-167, Minneapolis, USA, 2000.
  19. Sarwar, B., Karypis, G., Konstan, J., and Reidl, J., “Item-Based Collaborative Filtering Recommendation Algorithms,” Proceedings of the 10th Int’l WWW Conf., pp. 285-295, Hong Kong, May 1-5, 2001.
  20. Shardanand, U., and Maes, P., “Social Information Filtering : Algorithms for Automating ‘Word of Mouth’,” Proceedings of the ACM CHI Conf. on Human Factors in Computing Systems, pp. 210-217, Denver, USA, May 1995.
  21. Weng, J. et al., “Trust-Based Agent Community for Collaborative Recommendation,” Proceedings of AAMAS’06, pp. 1260-1262, Hakodate, Hokkaaido, Japan, May 8-12, 2006.