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Robust Collaborative Recommendation

Authors: 
Publication Type: 
Refereed Original Article
Abstract: 
Collaborative recommender systems are vulnerable to malicious users who seek to bias their output, causing them to recommend (or not recommend) particular items. This problem has been an active research topic since 2002. Researchers have found that the most widely-studied memory-based algorithms have significant vulnerabilities to attacks that can be fairly easily mounted. This chapter discusses these findings and the responses that have been investigated, especially detection of attack profiles and the implementation of robust recommendation algorithms.
Digital Object Identifer (DOI): 
10.1007/978-0-387-85820-3_25
Publication Status: 
Published
Publication Date: 
05/10/2010
Journal: 
Recommender Systems Handbook
Volume: 
2nd Edition.
Issue: 
Springer-Verlag (2015 expected)
Pages: 
pp 805-835
Research Group: 
Institution: 
National University of Ireland, Dublin (UCD)
Open access repository: 
No