Unsupervised Retrieval Of Attack Profiles In Collaborative Recommender Systems

Trust, reputation and recommendation are key components of successful e-commerce systems. However, e-commerce systems are also vulnerable in this respect because there are opportunities for sellers to gain advantage through manipulation of reputation and recommendation. One such vulnerability is the use of fraudulent user pro les to boost (or damage) the ratings of items in an online recommender system. In this paper we cast this problem as a problem of detecting anomalous structure in network analysis and propose a novel mechanism for detecting this anomalous structure. We present an evaluation that shows that this approach is e ective at uncovering the types of recommender systems attack described in the literature.