An improved collaborative filtering recommendation algorithm against shilling attacks

Abstract

Collaborative Filtering (CF) is a successful technology that has been implemented in E-commerce recommender systems. However, the risks of shilling attacks have already aroused increasing concerns of the society. Current solutions mainly focus on attack detection methods and robust CF algorithms that have flaws of unassured prediction accuracy. Furthermore, attack detection methods require a threshold to distinguish normal users from fake users and suffer from the problems of false positive if the threshold is too high and false negative if too low. This paper proposes a soft-decision method, Neighbor Selection with Variable-Length Partitions (VLPNS), to reduce false positive rate through marking suspicious fakers instead of deleting them directly such that misclassified normal users can still contribute to the similarity calculation. The method works as follows: First, it gets user’s suspicion probability by applying SVM. It then generates partitions of variable sizes from which different numbers of neighbors can be selected by using the bisecting c-means clustering algorithm. Finally, it chooses neighbors considering the user’s suspicion degree and similarity with target user at the same time. Theoretical and experimental analysis show that our approach ensures an excellent prediction accuracy against shilling attacks.