An Unsupervised Method for Detecting Shilling Attacks in Recommender Systems by Mining Item Relationship and Identifying Target Items

摘要

Collaborative filtering (CF) recommender systems have been shown to be vulnerable to shilling attacks. How to quickly and effectively detect shilling attacks is a key challenge for improving the quality and reliability of CF recommender systems. Although many recent studies have been devoted to detecting shilling attacks, there are still problems that require further discussion, especially the improvement of the detection performance on real-world unlabelled datasets. In this work, we propose an unsupervised approach that exploits item relationship and target item(s) for attack detection. We first extract behaviour features based on the item relationship. Then, we distinguish suspicious users from normal users and construct a set of suspicious users. Finally, we identify target item(s) by analysing the aggregation behaviour of suspicious users, based on which we detect attack users from the set of suspicious users. Extensive experiments on the MovieLens 100K dataset and sampled Amazon review dataset demonstrate the effectiveness of the proposed approach for detecting shilling attacks in recommender systems.