Robust Collaborative Recommendation

Collaborative recommender systems are vulnerable to malicious users who seek to bias their output, causing them to recommend (or not recommend) particular items. This problem has been an active research topic since 2002. Researchers have found that the most widely-studied memory-based algorithms have significant vulnerabilities to attacks that can be fairly easily mounted. This chapter discusses these findings and the responses that have been investigated, especially detection of attack profiles and the implementation of robust recommendation algorithms.

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

[2]  Padraig Cunningham,et al.  Unsupervised retrieval of attack profiles in collaborative recommender systems , 2008, RecSys '08.

[3]  Zunping Cheng,et al.  Effective diverse and obfuscated attacks on model-based recommender systems , 2009, RecSys '09.

[4]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[5]  Yan Tang,et al.  An effective recommender attack detection method based on time SFM factors , 2011, 2011 IEEE 3rd International Conference on Communication Software and Networks.

[6]  Thomas Hofmann,et al.  A Survey of Attack-Resistant Collaborative Filtering Algorithms , 2008, IEEE Data Eng. Bull..

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

[8]  Thomas Hofmann,et al.  Collaborative filtering via gaussian probabilistic latent semantic analysis , 2003, SIGIR.

[9]  John Riedl,et al.  Influence in Ratings-Based Recommender Systems: An Algorithm-Independent Approach , 2005, SDM.

[10]  张富国.  Preventing Recommendation Attack in Trust-Based Recommender Systems , 2011 .

[11]  Zhang Fu-guo,et al.  Analysis of Trust-Based E-Commerce Recommender Systems Under Recommendation Attacks , 2007, The First International Symposium on Data, Privacy, and E-Commerce (ISDPE 2007).

[12]  Bamshad Mobasher,et al.  Defending recommender systems: detection of profile injection attacks , 2007, Service Oriented Computing and Applications.

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

[14]  Paul Resnick,et al.  The influence limiter: provably manipulation-resistant recommender systems , 2007, RecSys '07.

[15]  Zheng Chen,et al.  Finding group shilling in recommendation system , 2005, WWW '05.

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

[17]  Sheng Huang,et al.  A Hybrid Decision Approach to Detect Profile Injection Attacks in Collaborative Recommender Systems , 2012, ISMIS.

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

[19]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[20]  Zhigang Luo,et al.  Detection of shilling attacks in collaborative filtering recommender systems , 2011, 2011 International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[21]  Bamshad Mobasher,et al.  Robustness of collaborative recommendation based on association rule mining , 2007, RecSys '07.

[22]  Neil J. Hurley,et al.  An Evaluation of Neighbourhood Formation on the Performance of Collaborative Filtering , 2004, Artificial Intelligence Review.

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

[24]  Bamshad Mobasher,et al.  Detecting Profile Injection Attacks in Collaborative Recommender Systems , 2006, The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06).

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

[26]  Huseyin Polat,et al.  Robustness analysis of privacy-preserving model-based recommendation schemes , 2014, Expert Syst. Appl..

[27]  Zunping Cheng,et al.  Trading Robustness for Privacy in Decentralized Recommender Systems , 2009, IAAI.

[28]  Benjamin Van Roy,et al.  Manipulation Robustness of Collaborative Filtering Systems , 2009, ArXiv.

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

[30]  John S. Baras,et al.  A Robust Collaborative Filtering Algorithm Using Ordered Logistic Regression , 2011, 2011 IEEE International Conference on Communications (ICC).

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

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

[33]  Chrysanthos Dellarocas,et al.  Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior , 2000, EC '00.

[34]  John Riedl,et al.  An Algorithmic Framework for Performing Collaborative Filtering , 1999, SIGIR Forum.

[35]  Paul Resnick,et al.  The information cost of manipulation-resistance in recommender systems , 2008, RecSys '08.

[36]  Mohammad Ali Abbasi,et al.  Trust-Aware Recommender Systems , 2014 .

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

[38]  Bhaskar Mehta,et al.  Unsupervised strategies for shilling detection and robust collaborative filtering , 2009, User Modeling and User-Adapted Interaction.

[39]  Robin Burke,et al.  Effective Attack Models for Shilling Item-Based Collaborative Filtering Systems , 2005 .

[40]  David C. Wilson,et al.  When power users attack: assessing impacts in collaborative recommender systems , 2013, RecSys.

[41]  Zunping Cheng,et al.  Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[42]  Sanjeev R. Kulkarni,et al.  Graph-based detection of shilling attacks in recommender systems , 2013, 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).

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

[44]  Robin Burke,et al.  Identifying Attack Models for Secure Recommendation , 2004 .

[45]  Thomas Hofmann,et al.  Lies and propaganda: detecting spam users in collaborative filtering , 2007, IUI '07.

[46]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

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

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

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