NDLI: Analysis of Bandwagon and Average Hybrid Attack Model against Trust-based Recommender Systems

Content Provider	IEEE Xplore Digital Library
Author	Fuguo Zhang
Copyright Year	2011
Abstract	Recommender systems have been accepted as a vital application on the web by offering product advice or information that users might be interested in. Despite its success, similarity-based collaborative filtering suffers from some significant limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. However, trust-based recommender systems are also known to be vulnerable to profile infection attacks. Malicious users can inject a large number of biased profiles into such a system in order to make recommendations that favor or disfavor given items. In this paper, we propose a bandwagon and average hybrid attack model and analysis the effectiveness of the attack model against topic-level trust-based recommender algorithm. The results of our experiments conducted on well-known dataset show that the hybrid attack model is more effective than other attack models.
Starting Page	269
Ending Page	273
File Size	322115
Page Count	5
File Format	PDF
ISBN	9781457716591
DOI	10.1109/ICMeCG.2011.10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-11-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	trust Analytical models shilling attack recommender system Databases Conferences Collaboration hybrid attack model Prediction algorithms Recommender systems
Content Type	Text
Resource Type	Article

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