Analysis of robustness in trust-based recommender systems

Much research has recently been carried out on the incorporation of trust models into recommender systems. It is generally understood that trust-based recommender systems can help to improve the accuracy of predictions. Moreover they provide greater robustness against profile injection attacks by malicious users. In this paper we analyze these contentions in the context of two trust-based algorithms. We note that one of the characteristics of trust-based algorithms is that ratings are often exposed in the user population in order for users to develop opinions on the trustworthiness of their peers. We will argue that exposing ratings presents a robustness vulnerability in these systems and we will show how this vulnerability can be exploited in the development of profile injection attacks. We conclude that the improved accuracy obtained in trust-based systems may well come at a cost of decreased robustness. In the end, trust models should be selected very carefully when building trust-based collaborative filtering (CF) systems.

[1]  A. Jøsang,et al.  Challenges for Robust Trust and Reputation Systems , 2009 .

[2]  Stephen Marsh,et al.  Formalising Trust as a Computational Concept , 1994 .

[3]  Neil J. Hurley,et al.  Collaborative recommendation: A robustness analysis , 2004, TOIT.

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

[5]  Thomas Hofmann,et al.  Robust collaborative filtering , 2007, RecSys '07.

[6]  Jordi Sabater-Mir,et al.  Review on Computational Trust and Reputation Models , 2005, Artificial Intelligence Review.

[7]  John Riedl,et al.  GroupLens: an open architecture for collaborative filtering of netnews , 1994, CSCW '94.

[8]  R. Burke,et al.  Limited Knowledge Shilling Attacks in Collaborative Filtering Systems , 2005 .

[9]  Robin Cohen,et al.  Smart cheaters do prosper: defeating trust and reputation systems , 2009, AAMAS.

[10]  Martin Ester,et al.  TrustWalker: a random walk model for combining trust-based and item-based recommendation , 2009, KDD.

[11]  Neil J. Hurley,et al.  Recommender Systems: Attack Types and Strategies , 2005, AAAI.

[12]  Jennifer Golbeck,et al.  Computing and Applying Trust in Web-based Social Networks , 2005 .

[13]  Barry Smyth,et al.  Trust in recommender systems , 2005, IUI.

[14]  R. Burke,et al.  Detection of Obfuscated Attacks in Collaborative Recommender Systems 1 , 2006 .

[15]  Neil J. Hurley,et al.  Promoting Recommendations: An Attack on Collaborative Filtering , 2002, DEXA.

[16]  Barry Smyth,et al.  Is trust robust?: an analysis of trust-based recommendation , 2006, IUI '06.

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

[18]  David Heckerman,et al.  Empirical Analysis of Predictive Algorithms for Collaborative Filtering , 1998, UAI.

[19]  Adam Tauman Kalai,et al.  Trust-based recommendation systems: an axiomatic approach , 2008, WWW.

[20]  Bamshad Mobasher,et al.  Model-Based Collaborative Filtering as a Defense against Profile Injection Attacks , 2006, AAAI.