Predictability Issues in Recommender Systems Based on Web Usage Behavior towards Robust Collaborative Filtering
This paper examines the effect of Recommender Systems in security oriented issues. Currently research has begun to evaluate the vulnerabilities and robustness of various collaborative recommender techniques in the face of profile injection and shilling attacks. Standard collaborative filtering algorithms are vulnerable to attacks. The robustness of recommender system and the impact of attacks are well suited this study and examined in this paper. The predictability issues and the various attack strategies are also discussed. Based on KNN the robustness of the recommender system were examined and the sensitivity of the rating given by the users are also analyzed. Furthermore the robust PLSA also considered for the work.
