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Defending shilling attacks in recommender systems using soft co-clustering

Defending shilling attacks in recommender systems using soft co-clustering

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Shilling attacks have been a significant vulnerability to collaborative filtering based recommender systems recently. There are various studies focusing on detecting shilling attack users and developing robust recommendation algorithms against shilling attacks. Although many studies have been devoted in this area, few of them use soft co-clustering and consider both labelled and unlabelled user profiles. In this work, the authors explore the benefits of combining soft co-clustering algorithm with user propensity similarity method and present a soft co-clustering with propensity similarity model or CCPS for short, to detect shilling attacks. Then they perform experiments using MovieLens dataset and Jester dataset to analyse it with respect to shilling attack detection to demonstrate the effectiveness of CCPS model in detecting traditional and hybrid shilling attacks and enhance the robustness of recommender systems.

References

    1. 1)
      • D. Cosley , S.K. Lam , I. Albert .
        1. Cosley, D., Lam, S.K., Albert, I., et al: ‘Is seeing believing? How recommender system interfaces affect users’ opinions’. ACM SIGCHI Int. Conf. on Human Factors in Computing Systems, Florida, USA, April 2003, pp. 585592.
        . ACM SIGCHI Int. Conf. on Human Factors in Computing Systems , 585 - 592
    2. 2)
      • J.S. Breese , D. Heckerman , C. Kadie .
        2. Breese, J.S., Heckerman, D., Kadie, C.: ‘Empirical analysis of predictive algorithms for collaborative filtering’. 14th Conf. on Uncertainty in Artificial Intelligence, Madison, WI, July 1998, pp. 4352.
        . 14th Conf. on Uncertainty in Artificial Intelligence , 43 - 52
    3. 3)
      • M. Deshpande , G. Karypis .
        3. Deshpande, M., Karypis, G.: ‘Item-based top-n recommendation algorithms’, ACM Trans. Inf. Syst., 2007, 22, (1), pp. 143177.
        . ACM Trans. Inf. Syst. , 1 , 143 - 177
    4. 4)
      • S.K. Lam , J. Riedl .
        4. Lam, S.K., Riedl, J.: ‘Shilling recommender systems for fun and profit’. Proc. Int. Conf. World Wide Web (WWW'04), New York, USA, May 2004, pp. 393402.
        . Proc. Int. Conf. World Wide Web (WWW'04) , 393 - 402
    5. 5)
      • B. Mehta , T. Hofmann , P. Fankhauser .
        5. Mehta, B., Hofmann, T., Fankhauser, P.: ‘Lies and propaganda: detecting spam users in collaborative filtering’. Proc. Int. Conf. Intelligent User Interfaces, Honolulu, HI, USA, January 2007, pp. 1421.
        . Proc. Int. Conf. Intelligent User Interfaces , 14 - 21
    6. 6)
      • R. Bhaumik , C. Williams , B. Mobasher .
        6. Bhaumik, R., Williams, C., Mobasher, B., et al: ‘Securing collaborative filtering against malicious attacks through anomaly detection’. 4th Workshop on Intelligent Techniques for Web Personalization (ITWP'06), Boston, MA, USA, July 2006.
        . 4th Workshop on Intelligent Techniques for Web Personalization (ITWP'06)
    7. 7)
      • B. Mobasher , R. Burke , R. Bhaumik .
        7. Mobasher, B., Burke, R., Bhaumik, R., et al: ‘Attacks and remedies in collaborative recommendation’, IEEE Intell. Syst., 2007, 22, (3), pp. 5663.
        . IEEE Intell. Syst. , 3 , 56 - 63
    8. 8)
      • P. Chirita , W. Nejdl , C. Zamfir .
        8. Chirita, P., Nejdl, W., Zamfir, C.: ‘Preventing shilling attacks in online recommender systems’. Proc. Int. Conf. 7th Annual ACM Int. Workshop on Web Information and Data Management, Washington, DC, USA, November 2005, pp. 6774.
        . Proc. Int. Conf. 7th Annual ACM Int. Workshop on Web Information and Data Management , 67 - 74
    9. 9)
      • B. Mehta .
        9. Mehta, B.: ‘Unsupervised shilling detection for collaborative filtering’. Proc. Int. Conf. Artificial Intelligence, Vancouver, BC, July 2007, pp. 14021407.
        . Proc. Int. Conf. Artificial Intelligence , 1402 - 1407
    10. 10)
      • X.F. Su , H.J. Zeng , Z. Chen .
        10. Su, X.F., Zeng, H.J., Chen, Z.: ‘Finding group shilling in recommendation system’. Proc. Int. Conf. World Wide Web, Chiba, Japan, May 2005, pp. 960961.
        . Proc. Int. Conf. World Wide Web , 960 - 961
    11. 11)
      • B. Mehta , W. Nejdl .
        11. Mehta, B., Nejdl, W.: ‘Unsupervised strategies for shilling detection and robust collaborative filtering’, User Model. User-Adapt. Interact., 2009, 19, (1-2), pp. 6597.
        . User Model. User-Adapt. Interact. , 65 - 97
    12. 12)
      • Z. Wu , J. Cao , B. Mao .
        12. Wu, Z., Cao, J., Mao, B., et al: ‘Semi-SAD: applying semi-supervised learning to shilling attack detection’. Proc. Int. Conf. Recommender Systems (RecSys2011), Chicago, IL, USA, October 2011, pp. 289292.
        . Proc. Int. Conf. Recommender Systems (RecSys2011) , 289 - 292
    13. 13)
      • X.-L. Zhang , T.M.D. Lee , G. Pitsilis .
        13. Zhang, X.-L., Lee, T.M.D., Pitsilis, G.: ‘Securing recommender systems against shilling attacks using social-based clustering’, J. Comput. Sci. Technol., 2013, 28, (4), pp. 616624.
        . J. Comput. Sci. Technol. , 4 , 616 - 624
    14. 14)
      • P. Chakraborty , S. Karforma .
        14. Chakraborty, P., Karforma, S.: ‘Detection of Profile-injection attacks in recommender systems using outlier analysis’, Proc. Technol., 2013, 10, pp. 963969.
        . Proc. Technol. , 963 - 969
    15. 15)
      • J.A. Hartigan .
        15. Hartigan, J.A.: ‘Direct clustering of a data matrix’, J. Am. Stat. Assoc., 1972, 67, (337), pp. 123129.
        . J. Am. Stat. Assoc. , 337 , 123 - 129
    16. 16)
      • P. Wang , C. Domeniconi , K.B. Laskey .
        16. Wang, P., Domeniconi, C., Laskey, K.B.: ‘Latent Dirichlet Bayesian co-clustering’. Machine Learning and Knowledge Discovery in Databases, 2009, pp. 522537.
        . Machine Learning and Knowledge Discovery in Databases , 522 - 537
    17. 17)
      • H. Shan , A. Banerjee .
        17. Shan, H., Banerjee, A.: ‘Bayesian co-clustering’. Proc. Int. Conf. Data Mining, Pisa, Italy, December 2008, pp. 530539.
        . Proc. Int. Conf. Data Mining , 530 - 539
    18. 18)
      • J.B. McDonald , Y.J. Xu .
        18. McDonald, J.B., Xu, Y.J.: ‘A generalization of the beta distribution with applications’, J. Econometrics, 1995, 66, (1), pp. 133152.
        . J. Econometrics , 1 , 133 - 152
    19. 19)
      • D. Blei , A. Ng , M. Jordan .
        19. Blei, D., Ng, A., Jordan, M.: ‘Latent Dirichlet allocation’, J. Mach. Learn. Res., 2003, 3, (Jan), pp. 9931022.
        . J. Mach. Learn. Res. , 993 - 1022
    20. 20)
      • A. Banerjee , S. Merugu , I. Dhillon .
        20. Banerjee, A., Merugu, S., Dhillon, I., et al: ‘Clustering with Bregman divergences’, J. Mach. Learn. Res., 2005, 6, (Oct), pp. 17051749.
        . J. Mach. Learn. Res. , 1705 - 1749
    21. 21)
      • G. Chen , F. Wang , C. Zhang .
        21. Chen, G., Wang, F., Zhang, C.: ‘Collaborative filtering using orthogonal nonnegative matrix tri-factorization’, Inf. Process. Manag., 2009, 45, (3), pp. 368379.
        . Inf. Process. Manag. , 3 , 368 - 379
    22. 22)
      • A. Banerjee , H. Shan .
        22. Banerjee, A., Shan, H.: ‘Latent Dirichlet conditional Naive-Bayes models’. Proc. Int. Conf. Data Mining, Washington, DC, USA, October 2007, pp. 421426.
        . Proc. Int. Conf. Data Mining , 421 - 426
    23. 23)
      • 23. MovieLens’, http://www.grouplens.org/datasets/movielens/, accessed 23 July 2016.
        .
    24. 24)
      • 24. Jester’, http://eigentaste.berkeley.edu/dataset/, accessed 23 July 2016.
        .
    25. 25)
      • D. Aggarwal , S. Merugu .
        25. Aggarwal, D., Merugu, S.: ‘Predictive discrete latent factor models for large scale dyadic data’. Proc. Int. Conf. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, America, 2007, pp. 2635.
        . Proc. Int. Conf. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 26 - 35
    26. 26)
      • N. Hurley , Z. Cheng , M. Zhang .
        26. Hurley, N., Cheng, Z., Zhang, M.: ‘Statistical attack detection’. Proc. Int. Conf. Recommender Systems (RecSys'09), New York, USA, October 2009, pp. 149156.
        . Proc. Int. Conf. Recommender Systems (RecSys'09) , 149 - 156
    27. 27)
      • M. O'Mahony , N. Hurley , N. Kushmerick .
        27. O'Mahony, M., Hurley, N., Kushmerick, N., et al: ‘Collaborative recommendation: a robustness analysis’, ACM Trans. Internet Technol., 2004, 4, (4), pp. 344377.
        . ACM Trans. Internet Technol. , 4 , 344 - 377
    28. 28)
      • C. Williams .
        28. Williams, C.: ‘Profile injection attack detection for securing collaborative recommender systems’. DePaul University CTI Technical Report, 2006, pp. 147.
        . , 1 - 47
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