Defending Suspected Ratings in Collaborative Filtering Recommender Systems: A Fast Detection Method

Zhihai Yang
2016 Proceedings of The fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)   unpublished
Collaborative filtering recommender systems (CFRSs) are key components of the well-known E-commerce websites such as Amazon, Yelp etc., to make personalized recommendations. In practice, CFRSs are highly vulnerable to "shilling" attacks. Detection methods based on such attacks have received much attention. However, their detection accuracy is not fully acceptable especially when the attack size or filler size is small. In this paper, we solve the following task: Given the rating dataset, how
more » ... we spot abnormal ratings of users as well as keeping reasonable time-consumption? We propose a fast and effective detection method to detect such attacks, which consists of two phases. We firstly capture all suspected users by employing a topk similarity method for calculating the similarity between users. Finally, we continue to filter out more genuine users by analysing target items as far as possible. In addition, extensive experiments demonstrate that the detection performance of our method outperforms benchmarked methods. It is noteworthy that the recently published attack, PIA (power item attack) including PIA-AS, PIA-ID and PIA-NR can be detected by our proposed method. 2015 18-19, December, ISCC
doi:10.22323/1.264.0015 fatcat:mrjz5uyowbhcfobh6b7jigoe34