Journal of Internet Computing and Services (인터넷정보학회논문지)

Korean Society for Internet Information (한국인터넷정보학회)
권호 표지

A Robust Collaborative Filtering against Manipulated Ratings

조작된 선호도에 강건한 협업적 여과 방법

  • 김흥남 (인하대학 BK21) ;
  • 하인애 (인하대학교 대학원 컴퓨터정보공학과) ;
  • 조근식 (인하대학교 컴퓨터정보공학과)
  • Published : 2009.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Collaborative filtering, one of the most successful technologies among recommender systems, is a system assisting users in easily finding the useful information and supporting the decision making. However, despite of its success and popularity, one notable issue is incredibility of recommendations by unreliable users called shilling attacks. To deal with this problem, in this paper, we analyze the type of shilling attacks and propose a unique method of building a model for protecting the recommender system against manipulated ratings. In addition, we present a method of applying the model to collaborative filtering which is highly robust and stable to shilling attacks.

협업적 여과는 추천 시스템을 구축하는데 가장 널리 보급된 정보 여과 기법으로 사용자 각 개인의 관심에 적합한 정보 및 아이템을 추천함으로써 사용자들의 의사 결정에 도움을 준다. 그러나 협업적 여과 기법은 우수한 추천 성능에도 불구하고, 최근에는 실링 공격이라 일컫는 악의적인 목적을 가진 사용자들의 추천 결과 조작에 쉽게 노출될 수 있는 문제가 새로운 이슈로 대두되고 있다. 본 논문에서는 협업적 여과의 실링 공격 문제들을 보완하기 위해, 추천 시스템에서 발생할 수 있는 실링 공격의 유형을 분석하고 악의적인 사용자의 조작된 선호도가 시스템에 미치는 영향을 최소화하기 위한 강건한 신뢰 모델 구축 방법을 제시한다. 그리고 그 모델을 적용하여 신뢰할 수 있는 아이템 추천 및 선호도 예측 방법을 제안한다.

Keywords

References

  1. Breese, J. S., Heckeman, D., and Kadie, C., "Empirical Analysis of Predictive Algorithms for Collaborative Filtering," Proceedings of the 14th Annual Conference on Uncertainty in ArtificialIntelligence, pp.43-52, 1998.
  2. Burke, R., Mobasher, B., Williams, C., and Bhaumik, R., "Classification Features for Attack Detection in Collaborative Recommender Systems," Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and DataMining, pp.542-547, 2006.
  3. Deshpande, M. and Karypis, G., "Item-based Top-N Recommendation Algorithms," ACM Transactions on Information Systems, Vol. 22, pp.143-177, 2004. https://doi.org/10.1145/963770.963776
  4. Herlocker, J. L., Konstan, J. A., Terveen, L. G., and Riedl, J. T., "Evaluating Collaborative Filtering Recommender Systems," ACM Transactions on Information Systems, Vol.22, ACM Press, pp.5-53, 2004. https://doi.org/10.1145/963770.963772
  5. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R., and Riedl, J., "GroupLens: Applying Collaborative Filtering to Usenet News," Communications of the ACM, Vol.40, pp.77-87, 1997. https://doi.org/10.1145/245108.245126
  6. Lam, S. and Ridl, J., "Shilling Recommender Systems for Fun and Profit," Proceedings of the 13th International World Wide Web Conference, ACM press, pp.393-402, 2004.
  7. Mobasher, B., Burke, R., Bhaumik, R., and Samdvig, J. J., "Attacks and Remedies in Collaborative Recommendation," IEEE Intelligent Systems, Vol.22, pp.56-63, 2007. https://doi.org/10.1109/MIS.2007.45
  8. Mobasher, B., Burke, R., Bhaumik, R., and Williams, C., "Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness," ACM Transactions on Internet Technology, Vol.7, 2007.
  9. O'Mahony, M., Hurley, N., Kushmerick, N., and Silverstre, G., "Collaborative Recommendation: A Robustness Analysis," ACM Transactions on Internet Technology, Vol.4, pp.344-377, 2004. https://doi.org/10.1145/1031114.1031116
  10. O'Donvan, J. and Smyth, B., "Mining Trust Values from Recommendation Errors," International Journal on Artificial Intelligence Tools, pp.945-962, 2006.
  11. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J., "GroupLens: An Open Architecture for Collaborative Filtering of Netnews," Proceedings of the ACM Conference on Computer Supported Cooperative Work, ACM press, pp.175-186, 1994.
  12. Sarwar, B., Karypis, G., Konstan, J., and Reidl, J., "Item-based Collaborative Filtering Recommendation Algorithms," Proceedings of the 10th International World Wide Web Conference, ACM press, pp.285-295, 2001.
  13. Shardanand, U. and Maes, P., "Social Information Filtering: Algorithms for Automating Word of Mouth," Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp.210-217, 1995.
  14. Williams, C., Mobasher, B., Burke, R., "Defending Recommender Systems: Detection of Profile Injection Attacks," Journal of Service Oriented Computing and Applications, Vol.1, Springer, pp.157-170, 2007. https://doi.org/10.1007/s11761-007-0013-0
  15. van Rijsbergen, C. J., “Information Retrieval,” 2nd Ed. Butterworth, 1979.