Estimating user behavior toward detecting anomalous ratings in rating systems☆
Introduction
Personalization collaborative filtering recommender systems (CFRSs) become more popular in the well-known E-commerce services such as Amazon, eBay, and etc. [6], [8], [24], [29], [51], [60], [62]. Abundant rating records are generated by customers on products or services. However, CFRSs are highly vulnerable to “profile injection” attacks (a.k.a. “shilling” attacks) [3], [9], [12], [19], [21], [25], [35], [43], [52], [55], [62]. It is a common occurrence that attackers contaminate the recommender systems with malicious ratings [14], [15]. They either demote a target item with the lowest rating (called nuke attack) or promote a target item with highest rating (called push attack) in order to achieve their attack intentions or decrease the quality of recommendation [6], [22], [31], [32], [33], [58]. Thus, developing an effective detection method to detect and remove the attackers before recommendation is crucial.
Detection methods based on the attacks have received much attention. Since the similarities between attackers are higher than genuine users, some of them have been presented based on calculating similarity between users [29], [41], [56]. Traditional similarity metrics including Pearson Correlation Coefficient (PCC), Cosine Similarity, and etc., can effectively capture the concerned attackers in some extent. However, the detection performance of these methods is largely relying on similarity calculation. How to reduce the time consumption of calculating similarity also is a hard issue, especially when facing large-scale datasets. Furthermore, some attackers mimic the rating details of some genuine users to improve their reliability. Only using similarity is difficult to fully discriminate. To address these challenges, a more effective detection method should be considered in the following aspects:
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Both the computation time and detection performance should be acceptable;
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It is effective to defense different kinds of “shilling” attacks.
In this paper, we propose an unsupervised detection method to spot such attacks, which consists of three stages. The goal of the proposed method is to filter out more genuine users and simultaneously keep all attackers step by step. To explore the similarity pattern of users from a new perspective, a novel graph mining algorithm [57] is employed for distinguishing attacker and genuine user. Naturally, an user-user undirected graph is firstly constructed from original user profiles. Furthermore, an edge between two vertexes (or users) in the graph is created when the number of the co-rated items of the two users is greater than an empirical threshold t. Moreover, during the constructing graph, a part of genuine users can be filtered out while retaining all attackers as far as possible. Based on the constructed graph, a fast and effective graph mining approach is used to calculate distance among vertexes (or users). Thus, a few genuine users can be further filtered out based on the calculated distances of users by exploiting a threshold of the distance, due to the fact that attackers and genuine users have different similarity patterns. Since co-rated items which used in the first two stages are only used to generate graph and calculate the distance among users, it is not enough to fully represent the difference between attack and genuine profiles. In reality, the effect of attacks is determined by both item and rating styles. Accordingly, analyzing target items with special ratings (i.e., the maximum and the minimum ratings) is investigated to capture the concerned attackers and further filter out the remained genuine users based on the result of the second stage. Finally, extensive experiments based on the MovieLens datasets demonstrate the effectiveness of the proposed method as compared to benchmark methods. A series of experiments in 14 different attacks also verify the detection performance of the proposed method.
The rest of the paper is organized as follows. Section 2 introduces related work. Section 3 shows the background of “shilling” attacks based on collaborative filtering recommendation. In Section 4, we detail the proposed method. In Section 5, experimental results are reported and analyzed. Finally, we conclude the paper with a brief summary and discuss the future work.
Section snippets
Related work
Discovering “shilling” attackers hidden in recommender systems is really crucial to enhance the quality and robustness of recommendation. A number of detection methods have been proposed so far, and they exhibit complementary advantage and disadvantage towards various types of attacks. In this section, we just discuss methods related to the present work in two aspects, supervised detection methods and unsupervised detection methods.
For the unsupervised detection methods, the difference between
Background
In this section, the background of collaborative filtering recommendation is firstly introduced. Then, the structure of attack profiles is detailed.
The proposed approach
In this section, the details of the proposed method are introduced in three stages including the stage of constructing graph, the stage of calculating similarity between vertexes in the graph and the stage of analyzing target items as shown in Fig. 1. In the first stage, an undirected user-user graph is constructed from original user profiles. In the second stage, the similarity (or distance) between vertexes is calculated by utilizing a graph mining method, which is used to distinguish
Experiments and analysis
In this section, the experimental settings are detailed at first. Extensive experiments based on the MovieLens datasets are conducted to examine the effectiveness of the proposed method including analyzing the detection performance compared with benchmark methods, detection results on diverse attacks and two different datasets. In addition, the computation time and time complexity of the presented methods and parameters sensitivity analysis are briefly discussed. Additional discussions are
Conclusion and future work
“Shilling” attacks are the main threats in collaborative filtering recommender systems. These attack profiles have a good probability of being similar rating details to a large number of genuine profiles in order to make them hard to be detected. In this paper, we proposed an unsupervised detection method for spotting the attacks (or anomalous ratings), which consists of three stages. Firstly, an user-user graph is constructed, which exploits the co-rated items rated by two users to create an
Acknowledgment
The research is supported by NSFC (61175039 and 61221063), 863 High Tech Development Plan (2012AA011003), Research Fund for Doctoral Program of Higher Education of China (20090201120032), International Research Collaboration Project of Shaanxi Province (2013KW11) and Fundamental Research Funds for Central Universities (2012jdhz08). Three anonymous reviewers have carefully read this paper and have provided to us numerous constructive suggestions. As a result, the overall quality of the paper has
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2023, Information SystemsCitation Excerpt :And the over-smoothing problem comes from the case that representations of nodes are unrelated to the input and converge to a station point when GCN has deep enough structure with more layers. Unlike the traditional detectors for model-generative shilling attacks [1,3,5,6,14–18] or group shilling attacks [9–11,19], in this work, we present a GCN-based detector for the hybrids of those shilling attacks without the additional knowledge about attack types. In particular, the user attributed graph is different from those in [9,17,21], in which the user features are comprehensively extracted by the sequence measurement and ratings statistics from user behaviors, and the edge weight is less likely to be affected by the number of the co-rated items.
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The research is supported by NSFC(61175039 and 61221063), 863 High Tech Development Plan (2012AA011003), Research Fund for Doctoral Program of Higher Education of China (20090201120032), International Research Collaboration Project of Shaanxi Province (2013KW11) and Fundamental Research Funds for Central Universities (2012jdhz08).