Abstract
Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method.
Highlights
With the development of e-commerce, information overload is a serious problem [1]
Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked
To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed
Summary
With the development of e-commerce, information overload is a serious problem [1]. As a kind of technology to generate recommendations by establishing a binary relationship between users and items, recommendation systems can alleviate the information overload problem effectively and have become a solution in information retrieval area. Shilling attack detection for user-based collaborative recommender systems this paper, ML100K, ML1M, Netflix and Eachmovie Datasets are most used in research papers on recommender systems. Detecting shilling attack profiles and eliminating adverse effects are the best methods to maintain the robustness of recommender systems. Causing their own products to be among the top rankings means money. Malicious users use faked identities to create user profiles and manipulate the recommendation list of a specific target item Injecting attack profiles to recommender system costs time and other resources. A shilling detection method based on the credibility of group users and rating time series is proposed. Shilling attack detection for user-based collaborative recommender systems method on different datasets to verify the model and algorithm. Experiments show that this method performs well in the detection of a large dataset of shilling attacks
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