Attack resistant collaborative filtering

The widespread deployment of recommender systems has lead to user feedback of varying quality. While some users faithfully express their true opinion, many provide noisy ratings which can be detrimental to the quality of the generated recommendations. The presence of noise can violate modeling assumptions and may thus lead to instabilities in estimation and prediction. Even worse, malicious users can deliberately insert attack profiles in an attempt to bias the recommender system to their benefit. While previous research has attempted to study the robustness of various existing Collaborative Filtering (CF) approaches, this remains an unsolved problem. Approaches such as Neighbor Selection algorithms, Association Rules and Robust Matrix Factorization have produced unsatisfactory results. This work describes a new collaborative algorithm based on SVD which is accurate as well as highly stable to shilling. This algorithm exploits previously established SVD based shilling detection algorithms, and combines it with SVD based-CF. Experimental results show a much diminished effect of all kinds of shilling attacks. This work also offers significant improvement over previous Robust Collaborative Filtering frameworks.