Robust Features for Trustable Aggregation of Online Ratings

When purchasing an online product, customers tend to be influenced strongly by its reputation, the aggregation of customers' ratings on the product. The reputation, however, is not always trustable since it can be easily manipulated by attackers. In this paper, we first address identifying trustable users on a given product in online rating systems, and computing its true reputation by aggregating only their ratings. In order to find these trustable users, we list candidate user features significantly related to the trustworthiness of users and verify the robustness of each user feature through extensive experiments.

[1]  John Riedl,et al.  Shilling recommender systems for fun and profit , 2004, WWW '04.

[2]  Sirkka L. Jarvenpaa,et al.  Perils of Internet fraud: an empirical investigation of deception and trust with experienced Internet consumers , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[3]  Moez Limayem,et al.  What makes consumers buy from Internet? A longitudinal study of online shopping , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Rachel Greenstadt,et al.  Using machine learning to augment collaborative filtering of community discussions , 2010, AAMAS.

[5]  Pierre Lévy.  L'intelligence collective : pour une anthropologie du cyberspace , 1994 .

[6]  M. Eisend.  Source Credibility Dimensions in Marketing Communication - A Generalized Solution , 2006 .

[7]  Huseyin Polat,et al.  Shilling attacks against recommender systems: a comprehensive survey , 2014, Artificial Intelligence Review.

[8]  Sunju Park,et al.  Trustable aggregation of online ratings , 2013, CIKM.

[9]  Soo Young Rieh,et al.  Credibility: A multidisciplinary framework , 2007, Annu. Rev. Inf. Sci. Technol..

[10]  Jong-Seok Lee,et al.  Shilling Attack Detection - A New Approach for a Trustworthy Recommender System , 2012, INFORMS J. Comput..

[11]  Fillia Makedon,et al.  Attack detection in time series for recommender systems , 2006, KDD '06.

[12]  Junjie Wu,et al.  HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation , 2012, KDD.

[13]  Bamshad Mobasher,et al.  Towards Trustworthy Recommender Systems : An Analysis of Attack Models and Algorithm Robustness , 2007 .

[14]  Valerie J. Trifts,et al.  Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids , 2000 .

[15]  Zunping Cheng,et al.  Statistical attack detection , 2009, RecSys '09.

[16]  A. Jøsang,et al.  Filtering Out Unfair Ratings in Bayesian Reputation Systems , 2004 .

[17]  Bamshad Mobasher,et al.  Classification features for attack detection in collaborative recommender systems , 2006, KDD '06.

[18]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[19]  Edmund A. Mennis.  The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations , 2006 .

[20]  Jie Cao,et al.  Semi-SAD: applying semi-supervised learning to shilling attack detection , 2011, RecSys '11.

[21]  Wolfgang Nejdl,et al.  Preventing shilling attacks in online recommender systems , 2005, WIDM '05.

[22]  Martin Eisend.  Source Credibility in Marketing Communication , 2006 .