βp: A novel approach to filter out malicious rating profiles from recommender systems

Decision Support Systems

Volumn 55, Issue 1, 2013, Pages 314-325

  Chung, Chen Yao ^a   Hsu, Ping Yu ^a   Huang, Shih Hsiang ^a,b  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b not available ^*  (Taiwan)

Author keywords

Collaborative filtering; Recommender systems; Shilling attacks detection

Indexed keywords

PROBABILITY DISTRIBUTIONS; RECOMMENDER SYSTEMS; STATISTICAL PROCESS CONTROL; WEBSITES;

ATTACK DETECTION; BETA DISTRIBUTIONS; CLASSIFICATION BASED METHODS; COLLABORATIVE FILTERING RECOMMENDER SYSTEMS; E-COMMERCE WEBSITES; STABLE PERFORMANCE; THEORETICAL FOUNDATIONS; USER-ITEM MATRIX;

COLLABORATIVE FILTERING;

EID: 84877767811     PISSN: 01679236     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.dss.2013.01.020     Document Type: Article

References (45)

1. GroupLens Lab, MovieLens Data Set http://www.grouplens.org/node/12 2010
2. G. Adomavicius, and A. Tuzhilin Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions IEEE Transactions on Knowledge and Data Engineering 17 6 2005 734 749
3. R. Barandela, J.S. Sánchez, V. García, and E. Rangel Strategies for learning in class imbalance problems Pattern Recognition 36 3 2003 849 851
4. R. Bhaumik, C. Williams, B. Mobasher, and R. Burke Securing collaborative filtering against malicious attacks through anomaly detection ITWP'06 Proceedings of the 4th Workshop on Intelligent Techniques for Web Personalization, at AAAI'06, Boston 2006
5. B. Burke, B. Mobasher, C. Williams, and R. Bhaumik Classification features for attack detection in collaborative recommender systems KDD '06 Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2006 ACM Press
6. R. Burke, B. Mobasher, C. Williams, and R. Bhaumik Detecting profile injection attacks in collaborative recommender systems CEC-EEE '06 Proceedings of the 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services 2006
7. J.A. Chevalier, and D. Mayzlin The effect of word of mouth on sales: online book reviews Journal of Marketing Research 43 3 2006 345 354
8. P.A. Chirita, W. Nejdl, and C. Zamfir Preventing shilling attacks in online recommender systems WIDM '05 Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management 2005 ACM Press
9. M. Deshpande, and G. Karypis Item-based top-N recommendation algorithms ACM Transactions on Information Systems 22 1 2004 143 177
10. N. Hu, I. Bose, Y. Gao, and L. Liu Manipulation in digital word-of-mouth: a reality check for book reviews Decision Support Systems 53 3 2011 627 635
11. N. Hu, L. Liu, and V. Sambamurthy Fraud detection in online consumer reviews Decision Support Systems 50 3 2011 614 626
12. N. Hu, I. Bose, N.S. Koh, and L. Liu Manipulation of online reviews: an analysis of ratings, readability, and sentiments Decision Support Systems 52 3 2012 674 684
13. N.J. Hurley, Z. Cheng, and M. Zhang Statistical attack detection RecSys '09 Proceedings of the third ACM conference on Recommender systems 2009 ACM Press
14. R. Jin, J.Y. Chai, and L. Si An automatic weighting scheme for collaborative filtering SIGIR '04 Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004
15. A. Jøsang, and R. Ismail The Beta reputation system Proceedings of the 15th Bled Conference on Electronic Commerce Conference 2002
16. A. Jøsang, S. Hird, and E. Faccer Simulating the effect of reputation systems on e-markets Proceedings of the First International Conference on Trust Management 2003
17. A. Jøsang, R. Ismail, and C. Boyd A survey of trust and reputation systems for online service provision Decision Support Systems 43 2 2007 618 644
18. F. Karakaya, and N.G. Barnes Impact of online reviews of customer care experience on brand or company selection Journal of Consumer Marketing 27 5 2010 447 457
19. H.N. Kim, A.T. Ji, H.J. Kim, and G.S. Jo Error-based collaborative filtering algorithm for top-N recommendation Lecture Notes in Computer Science 4505 2007 594 605
20. J. Konstan, B. Miller, D. Maltz, J. Herlocker, L. Gordon, and J. Riedl GroupLens: applying collaborative filtering to usenet news Communications of the ACM 40 3 1997 77 87
21. S. Kotsiantis, D. Kanellopoulos, and P. Pintelas Handling Imbalanced Datasets: a Review GESTS International Transactions on Computer Science and Engineering 2006
22. S.K. Lam, and J. Riedl Shilling recommender systems for fun and profit WWW '04 Proceedings of the 13th International Conference on World Wide Web 2004 ACM press
23. G. Linden, B. Smith, and J. York Amazon.com recommendations: item-to-item collaborative filtering IEEE Internet Computing 7 1 2003 76 80
24. P. Massa, and B. Bhattacharjee Using trust in recommender systems: an experimental analysis Lecture Notes in Computer Science 2995 2004 221 235
25. B. Mehta Unsupervised shilling detection for collaborative filtering AAAI '07 Proceedings of the 22nd National Conference on Artificial Intelligence 2007
26. B. Mehta, and W. Nejdl Unsupervised strategies for shilling detection and robust collaborative filtering User Modeling and User-Adapted Interaction 19 1-2 2009 65 97
27. B. Mehta, T. Hofmann, and P. Fankhauser Lies and propaganda: detecting spam users in collaborative filtering IUI '07 Proceedings of the 12th International Conference on Intelligent User Interfaces 2007
28. B. Mobasher, R. Burke, B. Bhaumil, and C. Williams Towards trustworthy recommender systems: an analysis of attack models and algorithm robustness ACM Transactions on Internet Technology 7 4 2007 23 38
29. S. Olsen Amazon blushes over sex link gaffe http://news.cnet.com/2100- 1023-976435.html 2002
30. M.P. O'Mahony, N.J. Hurley, and G.C.M. Silvestre Promoting recommendations: an attack on collaborative filtering Lecture Notes in Computer Science 2453 2002 494 503
31. M. O'Mahony, N.J. Hurley, N. Kushmerick, and G.C.M. Silvestre Collaborative recommendation: a robustness analysis ACM Transactions on Internet Technology 4 4 2004 344 377
32. M. Papagelis, D. Plexousakis, and T. Kutsuras Alleviating the sparsity problem collaborative filtering using trust inferences Lecture Notes in Computer Science 3477 2005 224 239
33. S. Piramuthu, G. Kapoor, W. Zhou, and S. Mauw Input online review data and related bias in recommender systems Decision Support Systems 53 3 2012 418 424
34. P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl GroupLens: an open architecture for collaborative filtering of netnews CSCW '94 Proceedings of Computer Supported Cooperative Work 1994
35. Joe Roth BBC News, Sony admits using fake reviewer http://news.bbc.co.uk/ 1/hi/entertainment/film/1368666.stm 2001
36. J. Rutledge, and B. Warner Using the Beta distribution on confidence intervals for proportions Proceedings of the Quality and Productivity Research Conference 1999
37. J.J. Sandvig, B. Mobasher, and R. Burke Robustness of collaborative recommendation based on association rule mining RecSys '07 Proceedings of the 2007 ACM Conference on Recommender Systems 2007 ACM press
38. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl Item-based collaborative filtering recommendation algorithms Proceedings of the 10th International World Wide Web Conference 2001 ACM Press
39. P.J. Sher, and S.H. Lee Consumer skepticism and online reviews: an elaboration likelihood model perspective Social Behavior and Personality 37 1 2009 137 144
40. C.T. Su, L.S. Chen, and Y. Yih Knowledge acquisition through information granulation for imbalanced data Expert Systems with Applications 31 3 2006 531 541
41. P.N. Tan, M. Steinbach, and V. Kumar Introduction to Data Mining 2006 Addison Wesley
42. A. Whitby, A. Jøsang, and J. Indulska Filtering out unfair ratings in bayesian reputation systems The Icfain Journal of Management Research 4 2 2005 48 64
43. C. Williams, B. Mobasher, R. Burke, R. Bhaumik, and J. Sandvig Detection of obfuscated attacks in collaborative recommender systems ECAI'06 Proceedings of the 17th European Conference on Artificial Intelligence 2006
44. L. Yu, L. Liu, and X.F. Li A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-commerce Expert Systems with Applications 28 1 2005 67 77
45. Z. Zhang, Q. Ye, R. Law, and Y. Li The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews International Journal of Hospitality Management 29 4 2010 694 700