Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems
Zhihai Yang and
Additional contact information
Zhihai Yang: Xi'an Jiaotong University, Xi'an, China
Zhongmin Cai: Xi'an Jiaotong University, Xi'an, China
International Journal of Digital Crime and Forensics (IJDCF), 2016, vol. 8, issue 2, 16-26
Online rating data is ubiquitous on existing popular E-commerce websites such as Amazon, Yelp etc., which influences deeply the following customer choices about products used by E-businessman. Collaborative filtering recommender systems (CFRSs) play crucial role in rating systems. Since CFRSs are highly vulnerable to â€œshillingâ€ attacks, it is common occurrence that attackers contaminate the rating systems with malicious rates to achieve their attack intentions. Despite detection methods based on such attacks have received much attention, the problem of detection accuracy remains largely unsolved. Moreover, few can scale up to handle large networks. This paper proposes a fast and effective detection method which combines two stages to find out abnormal users. Firstly, the manuscript employs a graph mining method to spot automatically suspicious nodes in a constructed graph with millions of nodes. And then, this manuscript continue to determine abnormal users by exploiting suspected target items based on the result of first stage. Experiments evaluate the effectiveness of the method.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJDCF.2016040102 (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:igg:jdcf00:v:8:y:2016:i:2:p:16-26
Access Statistics for this article
More articles in International Journal of Digital Crime and Forensics (IJDCF) from IGI Global
Bibliographic data for series maintained by Journal Editor ().