title: Detection of abnormal item based on time intervals for recommender systems.
abstract: 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.
authors.affiliation: School of Software Engineering, Chongqing University, Chongqing 400044, China ; Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400044, China.
authors.forename: Min
authors.identifier: 0000-0003-0127-7477
authors.initials: M
authors.lastname: Gao
authors.affiliation: School of Software Engineering, Chongqing University, Chongqing 400044, China.
authors.forename: Quan
authors.initials: Q
authors.lastname: Yuan
authors.affiliation: School of Engineering, University of Portsmouth, Portsmouth PO1 3AH, UK.
authors.forename: Bin
authors.initials: B
authors.lastname: Ling
authors.affiliation: School of Software Engineering, Chongqing University, Chongqing 400044, China ; Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing 400044, China.
authors.forename: Qingyu
authors.initials: Q
authors.lastname: Xiong
mesh_terms: D003258:Consumer Behavior; D008962:Models, Theoretical