Robust Collaborative Recommendation
Refereed Original Article
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):
Recommender Systems Handbook
Springer-Verlag (2015 expected)
National University of Ireland, Dublin (UCD)
Open access repository: