Unsupervised Retrieval of Attack Profiles in Collaborative Recommender Systems
|Title:||Unsupervised Retrieval of Attack Profiles in Collaborative Recommender Systems||Authors:||Bryan, Kenneth; O'Mahony, Michael P.; Cunningham, Pádraig||Permanent link:||http://hdl.handle.net/10197/12368||Date:||Apr-2008||Online since:||2021-07-30T12:27:30Z||Abstract:||Trust, reputation and recommendation are key components of successful ecommerce systems. However, ecommerce 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 profiles 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 effective at uncovering the types of recommender systems attack described in the literature.||Funding Details:||Science Foundation Ireland||Type of material:||Technical Report||Publisher:||University College Dublin. School of Computer Science and Informatics||Series/Report no.:||UCD CSI Technical Reports; ucd-csi-2008-3||Copyright (published version):||2008 the Authors||Keywords:||Recommender systems; E-commerce; Online attacks; Attack detection||Other versions:||https://web.archive.org/web/20080226040105/http:/csiweb.ucd.ie/Research/TechnicalReports.html||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science and Informatics Technical Reports|
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