Detection of Abnormal Item Based on Time Intervals for Recommender Systems
Joint Authors
Ling, Bin
Xiong, Qingyu
Gao, Min
Yuan, Quan
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-12
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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 (χ2).
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.
American Psychological Association (APA)
Gao, Min& Yuan, Quan& Ling, Bin& Xiong, Qingyu. 2014. Detection of Abnormal Item Based on Time Intervals for Recommender Systems. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051320
Modern Language Association (MLA)
Gao, Min…[et al.]. Detection of Abnormal Item Based on Time Intervals for Recommender Systems. The Scientific World Journal No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1051320
American Medical Association (AMA)
Gao, Min& Yuan, Quan& Ling, Bin& Xiong, Qingyu. Detection of Abnormal Item Based on Time Intervals for Recommender Systems. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1051320
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051320