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Enhanced Prediction Algorithm for Item-Based Collaborative Filtering Recommendation

  • Heung-Nam Kim
  • Ae-Ttie Ji
  • Geun-Sik Jo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4082)

Abstract

As the Internet infrastructure has been developed, a substantial number of diverse effective applications have attempted to achieve the full potential offered by the infrastructure. Collaborative Filtering recommender system, one of the most representative systems for personalized recommendations in E-commerce on the Web, is a system assisting users in easily finding the useful information. But traditional collaborative filtering suffers some weaknesses with quality evaluation: the sparsity of the data, scalability, unreliable users. To address these issues, we have presented a novel approach to provide the enhanced prediction quality supporting the protection against the influence of malicious ratings, or unreliable users. In addition, an item-based approach is employed to overcome the sparsity and scalability problems. The proposed method combines the item confidence and item similarity, collectively called item trust using this value for online predictions. The experimental evaluation on MovieLens datasets shows that the proposed method brings significant advantages both in terms of improving the prediction quality and in dealing with malicious datasets.

Keywords

Neighborhood Size Collaborative Filter Prediction Quality Collaborative Filter Algorithm Item Trust 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proc. of the 14th Conf. on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)Google Scholar
  2. 2.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proc. of the 10th Int. Conf. on World Wide Web (2001)Google Scholar
  3. 3.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: Proc. of the ACM Conf. on Computer supported Cooperative Work, pp. 175–186 (1994)Google Scholar
  4. 4.
    Lemire, D., Maclachlan, A.: Slope One Predictors for Online Rating-Based Collaborative Filtering. In: Proc. of SIAM Data Mining (2005)Google Scholar
  5. 5.
    Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22, 143–177 (2004)CrossRefGoogle Scholar
  6. 6.
    Massa, P., Avesani, P.: Trust-aware Collaborative Filtering for Recommender Systems. In: Proc. of Int. Conf. on Cooperative Information Systems (2004)Google Scholar
  7. 7.
    Papagelis, M., Plexousakis, D., Kutsuras, T.: Alleviation the Sparsity Problem of Collaborative Filtering Using Trust Inferences. In: Herrmann, P., Issarny, V., Shiu, S.C.K. (eds.) iTrust 2005. LNCS, vol. 3477, pp. 224–239. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: Proc. of the 10th Int. Conf. on Intelligent user interfaces, pp. 167–174 (2005)Google Scholar
  9. 9.
    Mobasher, B., Jin, X., Zhou, Y.: Semantically Enhanced Collaborative Filtering On the Web. In: Berendt, B., Hotho, A., Mladenič, D., van Someren, M., Spiliopoulou, M., Stumme, G. (eds.) EWMF 2003. LNCS, vol. 3209, pp. 57–76. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Kim, H.J., Jung, J.J., Jo, G.S.: Conceptual Framework for Recommendation System based on Distributed User Ratings. In: Li, M., Sun, X.-H., Deng, Q.-n., Ni, J. (eds.) GCC 2003. LNCS, vol. 3032, pp. 115–122. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for E-commerce. In: Proc. of ACM 2000 Conf. on Electronic Commerce, pp. 158–167 (2000)Google Scholar
  12. 12.
    Miller, B.N., Konstan, J.A., Riedl, J.: PocketLens: Toward a personal recommender system. ACM Transactions on Information Systems 22, 437–476 (2004)CrossRefGoogle Scholar
  13. 13.
    Schein, A.I., Popescul, A., Ungar, L.H.: Methods and Metrics for Cold-Start Recommendations. In: Proc. of the 25th Int. ACM Conf. on Research and Development in Information Retrieval (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Heung-Nam Kim
    • 1
  • Ae-Ttie Ji
    • 1
  • Geun-Sik Jo
    • 2
  1. 1.Intelligent E-Commerce Systems Laboratory, Department of Computer Science & Information EngineeringInha University 
  2. 2.School of Computer Science & EngineeringInha UniversityIncheonKorea

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