Detection of Abnormal Item Based on Time Intervals for Recommender Systems

With the rapid development of e-business, personalized recommendation has become core competence for enterprises to gain profits and improve customer satisfaction. Although collaborative filtering is the most successful approach for building a recommender system, it suffers from “shilling” attacks. In recent years, the research on shilling attacks has been greatly improved. However, the approaches suffer from serious problem in attack model dependency and high computational cost. To solve the problem, an approach for the detection of abnormal item is proposed in this paper. In the paper, two common features of all attack models are analyzed at first. A revised bottom-up discretized approach is then proposed based on time intervals and the features for the detection. The distributions of ratings in different time intervals are compared to detect anomaly based on the calculation of chi square distribution (χ 2). We evaluated our approach on four types of items which are defined according to the life cycles of these items. The experimental results show that the proposed approach achieves a high detection rate with low computational cost when the number of attack profiles is more than 15. It improves the efficiency in shilling attacks detection by narrowing down the suspicious users.

[1]  Bamshad Mobasher,et al.  Analysis and Detection of Segment-Focused Attacks Against Collaborative Recommendation , 2005, WEBKDD.

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

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

[4]  Randy Kerber,et al.  ChiMerge: Discretization of Numeric Attributes , 1992, AAAI.

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

[6]  Hui Xiong,et al.  Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[8]  MehtaBhaskar,et al.  Unsupervised strategies for shilling detection and robust collaborative filtering , 2009 .

[9]  Kecheng Liu,et al.  Prioritised Stakeholder Analysis for Software Service Lifecycle Management , 2013, 2013 IEEE 20th International Conference on Web Services.

[10]  Yanchun Zhang,et al.  Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system , 2013, World Wide Web.

[11]  Neil J. Hurley,et al.  Robust Collaborative Recommendation , 2011, Recommender Systems Handbook.

[12]  Fillia Makedon,et al.  Attack detection in time series for recommender systems , 2006, KDD '06.

[13]  Bamshad Mobasher,et al.  Detecting Profile Injection Attacks in Collaborative Filtering: A Classification-Based Approach , 2006, WEBKDD.