Abstract
Collaborative filtering is a vitally central technology of personalized recommendation, yet its recommended result is so sensitive to users’ preferences that the recommender system has significant vulnerabilities. To overcome the addressed issue, this paper proposes a hybrid decision approach to effectively and efficiently detect profile injection attacks in collaborative recommender systems. Through modifying the algorithms of RDMA (Rating Deviation from Mean Agreement) and WDMA (Weighted Deviation form Mean Agreement), the hybrid decision approach is integrated from these modified algorithms and the UnRAP (Unsupervised Retrieval of Attack Profiles) algorithm. The extensive experiments based on three common attack models show that the proposed detection algorithm is the best comparing with the modified RDMA and WDMA or origin ones, by which the detecting accuracy significantly increases almost 35%, 25%, and 8% than the RMDA, WMDA, and UnRAP algorithms, respectively. Furthermore, for the mixed attack model, we compare it with the UnRAP algorithm and improve the 10% accuracy.
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References
Mobasher, B., Burke, R., Bhaumik, R., Williams, C.: Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology 7(4), 23 (2007)
Chirita, P., Nejdl, W., Zamr, C.: Preventing shilling attacks in online recommender systems. In: ACM WIDM, pp. 67–74 (2005)
Su, X., Zeng, H., Chen, Z.: Finding group shilling in recommendation system. In: WWW, pp. 960–961 (2005)
Williams, C., Mobasher, B., Burke, R.: Defending recommender systems: Detection of profile injection attacks. Service Oriented Computing and Applications 1(3), 57–170 (2007)
Mobasher, B., Burke, R., Bhaumik, R.: Effective attack models for shilling item-based collaborative filtering systems. In: ACM WebKDD (2005)
O’Mahony, M., Hurley, N., Silvestre, G.: Utility-based neighborhood formation for efficient and robust collaborative filtering. In: ACM EC, pp. 260–261 (2004)
Hurley, N., Cheng, Z., Zhang, M.: Statistical attack detection. In: ACM RecSys, pp. 149–156 (2009)
Mehta, B., Nejdl, W.: Unsupervised strategies for shilling detection and robust collaborative filtering. User Modeling and User-Adapted Interaction 19(1), 65–79 (2009)
Bryan, K., O’Mahony, M., Cunningham, P.: Unsupervised retrieval of attack proles in collaborative recommender systems. In: ACM RecSys, pp. 155–162 (2008)
Burke, R., Mobasher, B., Williams, C., Bhaumik, R.: Classification features for attack detection in collaborative recommender systems. In: ACM SIGKDD, pp. 542–547 (2006)
Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: ACM SIGKDD, pp. 397–406 (2009)
Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks. In: ACM RecSys, pp. 135–142 (2010)
Au Yeung, C., Iwata, T.: Strength of social influence in trust networks in product review sites. In: ACM WSDM, pp. 495–504 (2011)
Cha, M., Haddadi, H., Benevenuto, F., Gummad, K.P.: Measuring user influence on twitter: The million follower fallacy. In: ICWSM (2010)
Pu, P., Chen, L., Hu, R.: A user-centric evaluation framework for recommender systems. In: ACM RecSys, pp. 157–164 (2011)
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Huang, S., Shang, M., Cai, S. (2012). A Hybrid Decision Approach to Detect Profile Injection Attacks in Collaborative Recommender Systems. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science(), vol 7661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34624-8_43
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DOI: https://doi.org/10.1007/978-3-642-34624-8_43
Publisher Name: Springer, Berlin, Heidelberg
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