Recommendation Algorithm

Yıl 2015, Cilt: 4 Sayı: 1, 13 - 25, 31.03.2015

İhsan Gunes , Huseyin Polat

Öz

Robustness Analysis of Privacy-Preserving Hybrid Recommendation Algorithm

Öz

In addition to memory- and model-based recommendation schemes, hybrid methods are widely used due to their advantages. Such schemes should be able to provide accurate predictions efficiently while preserving privacy. Also, they need to be robust against possible profile injection or shilling attacks. Although some privacy-preserving memory- and model-based collaborative filtering algorithms have been investigated with respect to robustness, privacy-preserving hybrid recommendation schemes have not been analyzed in terms of robustness.

In this paper, we analyze a privacy-preserving hybrid collaborative filtering scheme with respect to robustness. Four push and two nuke attack models are applied to the algorithm in order to show how robust it is against such shilling attacks. Different sets of experiments are conducted using real data to show how varying controlling parameters affect the robustness. The hybrid scheme is compared with memory- and model-based scheme in terms of robustness. Our analysis show that although the scheme can be marginally considered as robust algorithm, it is less robust than memory- or model-based prediction algorithms with privacy.

Anahtar Kelimeler

Robustness, hybrid algorithm, privacy, shilling attacks, recommendation

Kaynakça

• C. Birtolo and D. Ronca, “Advances in Clustering Collaborative Filtering by Means of Fuzzy C-means and Trust,” Expert Systems with Applications, vol. 40, no. 17, pp. 6997-7009, 2013.
• J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérez, “Recommender Systems Survey,” Knowledge-Based Systems, vol. 46, pp. 109-132, 2013.
• J. L. Herlocker, J. A. Konstan, L. G. Terveen, J. T. Riedl, “Evaluating Systems,” ACM Transactions on Internet Technology, vol. 22, no. 1, pp. 5-53, 2004.
• H. Polat and W. Du, “SVD-based collaborative filtering with privacy,” in Proceedings of the 2005 ACM Symposium on Applied Computing, Santa Fe, NM, USA, pp. 791-795, 2005.
• H. Polat and W. Du, “Privacy-Preserving Collaborative Filtering,” Commerce, vol. 9, no. 4, pp. 9-35, 2005. Journal of Electronic
• A. Bilge and H. Polat, “An Improved Privacy-preserving DWT-based Collaborative Filtering Scheme,” Expert Systems with Applications, vol. 39, no. 3, pp. 3841-3854, 2012.
• A. Bilge and H. Polat, “A Comparison of Clustering- based Schemes,” Applied Soft Computing, vol. 13, no. 5, pp. 2478-2489, 2013. Collaborative Filtering
• S. Renckes, H. Polat, Y. Oysal, “A New Hybrid Recommendation Algorithm with Privacy,” Expert Systems, vol. 29, no. 1, pp. 39-55, 2012.
• R. Burke, B. Mobasher, R. Zabicki, R. Bhaumik, “Identifying attack models for secure recommendation,” in Proceedings of the Beyond Personalization: The Next Stage of Recommender Systems Research Work shop in conjuction with the International Conference on Intelligent User Interfaces, San Diego, CA, USA, 2005.
• R. Burke, M. P. O’Mahony, N. J. Hurley, “Robust Collaborative Recommendation,” in Recommender Systems Handbook , Springer US, pp. 805-835, 2011.
• B. Mobasher, R. Burke, R. Bhaumik, C. Williams, “Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness,” ACM Transactions on Internet Technology, vol. 7, no. 4, Article No 23, 38 pages, 2007.
• J. Canny, “Collaborative filtering with privacy,” in Proceedings of the 2002 IEEE Symposium on Security and Privacy, Berkeley, CA, USA, 45-57, 2002.
• A. Bilge, I. Gunes, H. Polat, “Robustness Analysis of Privacy-preserving Schemes,” Expert Systems with Applications, vol. 41, no. 8, pp. 3671-3681, 2014.
• Recommendation Attacks Filtering,” Journal of Advanced Management Science, vol. 1, no. 1, pp. 54-60, 2013. Collaborative
• I. Gunes, A. Bilge, H. Polat, “Shilling Attacks against Memory-based Algorithms,” KSII Transactions on Internet & Information Systems, vol. 7, no. 5, pp. 1272-1290, 2013.
• M. P. O’Mahony, N. J. Hurley, G. C. Silvestre, “Towards Robust Collaborative Filtering,” Lecture Notes in Computer Science, vol. 2464, pp. 87-94, 2002.
• M. P. O’Mahony, N. J. Hurley, G. C. Silvestre, “Promoting collaborative filtering," in Proceedings of the 13th International Conference on Database and Expert Systems Applications, Aix-en-Provence, France, pp. 494- 503, 2002. An attack on
• B. Marlin, “Collaborative Filtering: A Machine Learning Perspective,” MSc. Thesis, University of Toronto, 2004.
• G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, Z. Chen, “Scalable collaborative filtering using cluster- based smoothing,” in Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil, pp. 114-121, 2005.
• J. L. Herlocker, J. A. Konstan, A. Borchers, J. Riedl, “An algorithmic framework for performing collaborative filtering,” in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, CA, USA, pp. 230-237, 1999.