A Shilling Attack Detection Method Based on SVM and Target Item Analysis in Collaborative Filtering Recommender Systems
Wei Guo Zhou, Junhao Wen, Min Gao, Ling Liu, Haini Cai, Xibin Wang · Lecture notes in computer science · 2015
The open nature of recommender systems makes them vulnerable to shilling attacks. Biased ratings are introduced in order to affect recommendations, have been shown to cause great harm to collaborative filtering algorithms. Most of previous research focuses on the differences between genuine profiles and attack profiles, ignoring the group characteristics in an attack. There exists class unbalance problems in SVM based detecting methods, that is, the detecting performance is not good when the amount of samples of attack profiles in training set is small. In this paper, we study the use of SVM based method and group characteristics in attack profiles to detect attack profiles. Based on this, a two phase detecting method SVM-TIA is proposed. In the first phase, Borderline-SMOTE method is used to alleviate the class unbalance problem in classification; a rough detecting result is obtained in this phase; the second phase is a fine-tuning phase whereby the target items in the potential attack profiles set are analysed. We conduct experiments on the MovieLens 100K Dataset and compare the performance of SVM-TIA with other shilling detecting methods to demonstrate the effectiveness of the proposed approach.