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Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems

Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems

Zhihai Yang, Zhongmin Cai
Copyright: © 2016 |Volume: 8 |Issue: 2 |Pages: 11
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781466690257|DOI: 10.4018/IJDCF.2016040102
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MLA

Yang, Zhihai, and Zhongmin Cai. "Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems." IJDCF vol.8, no.2 2016: pp.16-26. http://doi.org/10.4018/IJDCF.2016040102

APA

Yang, Z. & Cai, Z. (2016). Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems. International Journal of Digital Crime and Forensics (IJDCF), 8(2), 16-26. http://doi.org/10.4018/IJDCF.2016040102

Chicago

Yang, Zhihai, and Zhongmin Cai. "Detecting Anomalous Ratings in Collaborative Filtering Recommender Systems," International Journal of Digital Crime and Forensics (IJDCF) 8, no.2: 16-26. http://doi.org/10.4018/IJDCF.2016040102

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Abstract

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 “shilling” 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.

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