A Robust Collaborative Filtering against Manipulated Ratings

Heung-Nam Kim; In-Ay Ha; Geun-Sik Jo

A Robust Collaborative Filtering against Manipulated Ratings

Heung-Nam Kim, In-Ay Ha, Geun-Sik Jo, Journal of Internet Computing and Services, Vol. 10, No. 6, pp. 81-98, Dec. 2009
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Keywords: Collaborative Filtering, Recommender System, shilling attack, manipulated rating

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.

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Cite this article

[APA Style]

Kim, H., Ha, I., & Jo, G. (2009). A Robust Collaborative Filtering against Manipulated Ratings. Journal of Internet Computing and Services, 10(6), 81-98.

[IEEE Style]

H. Kim, I. Ha, G. Jo, "A Robust Collaborative Filtering against Manipulated Ratings," Journal of Internet Computing and Services, vol. 10, no. 6, pp. 81-98, 2009.

[ACM Style]

Heung-Nam Kim, In-Ay Ha, and Geun-Sik Jo. 2009. A Robust Collaborative Filtering against Manipulated Ratings. Journal of Internet Computing and Services, 10, 6, (2009), 81-98.