Title :
Analysis of Love-Hate Shilling Attack Against E-commerce Recommender System
Author_Institution :
Sch. of Inf. Technol., Jiangxi Univ. of Finance & Econ., Nanchang, China
Abstract :
Recent research has focus on examining the security of e-commerce collaborative filtering(CF) recommender system. Love/hate attack is one of the most effective model as a nuke attack against the classic user-based CF. In this paper, we examine the effectiveness of Love/hate attack against our topic-level trust based recommendation algorithm that incorporate topic-level trust model into traditional collaborative filtering algorithm. The results of our experiments conducted on well-known dataset show that Love/hate attack is more robust against topic-level trust based recommendation algorithm than against classical user-based CF algorithm.
Keywords :
electronic commerce; groupware; recommender systems; security of data; e-commerce collaborative filtering recommender system; love-hate shilling attack; topic-level trust model; Algorithm design and analysis; Biological system modeling; Collaboration; Prediction algorithms; Recommender systems; Robustness; collaborative filtering; love/hate attack; recommender system; topic-level trust;
Conference_Titel :
Information Science and Management Engineering (ISME), 2010 International Conference of
Conference_Location :
Xi´an
Print_ISBN :
978-1-4244-7669-5
Electronic_ISBN :
978-1-4244-7670-1
DOI :
10.1109/ISME.2010.116
