Visible to the public Inference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender Systems

TitleInference of Suspicious Co-Visitation and Co-Rating Behaviors and Abnormality Forensics for Recommender Systems
Publication TypeJournal Article
Year of Publication2020
AuthorsYang, Z., Sun, Q., Zhang, Y., Zhu, L., Ji, W.
JournalIEEE Transactions on Information Forensics and Security
Volume15
Pagination2766—2781
ISSN1556-6021
Keywordsabnormality forensics, anomaly detection, association rules, attack detection, co-rating behaviors, co-rating graphs, Collaboration, Couplings, data mining, e-commerce services, electronic commerce, Forensics, forensics metrics including distribution, fundamental vulnerabilities, graph theory, historical ratings, Human Behavior, inference, information forensics, malicious attack, malicious attack behaviors, malicious users, Measurement, Metrics, personalized collaborative recommender systems, pervasiveness, pubcrawl, rating intention, real-world data, recommender system, recommender systems, resilience, Resiliency, Scalability, security of data, spotting anomalies, structure-based property, suspicious co-visitation behavior, suspicious nodes, suspicious ratings, time series, Time series analysis, ubiquitous computing, unified detection framework
AbstractThe pervasiveness of personalized collaborative recommender systems has shown the powerful capability in a wide range of E-commerce services such as Amazon, TripAdvisor, Yelp, etc. However, fundamental vulnerabilities of collaborative recommender systems leave space for malicious users to affect the recommendation results as the attackers desire. A vast majority of existing detection methods assume certain properties of malicious attacks are given in advance. In reality, improving the detection performance is usually constrained due to the challenging issues: (a) various types of malicious attacks coexist, (b) limited representations of malicious attack behaviors, and (c) practical evidences for exploring and spotting anomalies on real-world data are scarce. In this paper, we investigate a unified detection framework in an eye for an eye manner without being bothered by the details of the attacks. Firstly, co-visitation and co-rating graphs are constructed using association rules. Then, attribute representations of nodes are empirically developed from the perspectives of linkage pattern, structure-based property and inherent association of nodes. Finally, both attribute information and connective coherence of graph are combined in order to infer suspicious nodes. Extensive experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed detection approach compared with competing benchmarks. Additionally, abnormality forensics metrics including distribution of rating intention, time aggregation of suspicious ratings, degree distributions before as well as after removing suspicious nodes and time series analysis of historical ratings, are provided so as to discover interesting findings such as suspicious nodes (items or ratings) on real-world data.
DOI10.1109/TIFS.2020.2977023
Citation Keyyang_inference_2020