Probabilistic Inference and Trustworthiness Evaluation of Associative Links toward Malicious Attack Detection for Online Recommendations
Zhihai Yang, Qindong Sun, Yaling Zhang · IEEE Transactions on Dependable and Secure Computing · 2020
The increasing use of recommender systems as personalization recommendation services such as Amazon, TripAdvisor, and Yelp, has stressed the demand for secure and usable abnormality detection techniques, due to fundamental vulnerabilities of recommender systems and their openness. With the emergence of new attacks, how to defend diverse malicious attacks for online recommendations is a challenging issue. Moreover, characterizing and evaluating sparse rating behaviors are a long-standing problem that still remains open, leading to an upsurge of research, as well as real application. This article investigates probabilistic inference and trustworthiness evaluation of behavioral links according to coupled association networks converted from rating behaviors, and presents a unified detection framework from a novel perspective to spot diverse malicious threats. First, an association graph is constructed from the original rating matrix based on both the inherent rating motivation of users and atomic propagation rules of coupled networks. Then, we evaluate the trustworthiness of link behaviors in the targeted network of coupled association network by exploiting a factor graph model of coupled network, and redetermine concerned links in the targeted network. Finally, suspicious users and items can be empirically inferred by comprehensively evaluating the trustworthiness of both links and nodes in the targeted network. Extensive experiments on synthetic data for profile injection attacks and co-visitation injection attacks, as well as real-world data including Amazon and TripAdvisor, demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks.