Multiview Ensemble Method for Detecting Shilling Attacks in Collaborative Recommender Systems

Yaojun Hao,Fuzhi Zhang,Peng Zhang

doi:10.1155/2018/8174603

Abstract

Faced with the evolving attacks in collaborative recommender systems, the conventional shilling detection methods rely mainly on one kind of user-generated information (i.e., single view) such as rating values, rating time, and item popularity. However, these methods often suffer from poor precision when detecting different attacks due to ignoring other potentially relevant information. To address this limitation, in this paper we propose a multiview ensemble method to detect shilling attacks in collaborative recommender systems. Firstly, we extract 17 user features by considering the temporal effects of item popularity and rating values in different popular item sets. Secondly, we devise a multiview ensemble detection framework by integrating base classifiers from different classification views. Particularly, we use a feature set partition algorithm to divide the features into several subsets to construct multiple optimal classification views. We introduce a repartition strategy to increase the diversity of views and reduce the influence of feature order. Finally, the experimental results on the Netflix and Amazon review datasets indicate that the proposed method has better performance than benchmark methods when detecting various synthetic attacks and real-world attacks.

Highlights

Collaborative recommender systems are widely used in ecommerce websites to handle the information overload problem by providing personalized recommendations for their users
Where TP denotes the number of shilling profiles correctly classified, FN denotes the number of shilling profiles misclassified as genuine ones, and FP denotes the number of genuine profiles misclassified as shilling ones
Based on the proposed features, we improve the features set partition algorithm to combine it with the kNN base classifiers, and design the multiview ensemble method to detect various shilling profiles

Summary

Introduction

Collaborative recommender systems are widely used in ecommerce websites to handle the information overload problem by providing personalized recommendations for their users. Due to the openness of such systems, the attackers are likely to inject a large number of fake profiles in order to increase/decrease the recommendation frequency of particular items (e.g., movies and products). This behaviour is often referred to as shilling attacks or profile injection attacks. The fake profiles are called attack profiles or shilling profiles, which have a negative impact on the prediction quality of collaborative recommender system and make the users lose trust in the system. An attack is realized by inserting several shilling profiles into a recommender system database to cause bias on selected target items [30].

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Conclusion