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» Attacks and Remedies in Collaborative Recommendation
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DEBU
2008
186views more  DEBU 2008»
13 years 7 months ago
A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms
The open nature of collaborative recommender systems allows attackers who inject biased profile data to have a significant impact on the recommendations produced. Standard memory-...
Jeff J. Sandvig, Bamshad Mobasher, Robin D. Burke
INFOCOM
2010
IEEE
13 years 5 months ago
Predictive Blacklisting as an Implicit Recommendation System
A widely used defense practice against malicious traffic on the Internet is to maintain blacklists, i.e., lists of prolific attack sources that have generated malicious activity in...
Fabio Soldo, Anh Le, Athina Markopoulou
ECWEB
2007
Springer
162views ECommerce» more  ECWEB 2007»
14 years 1 months ago
Impact of Relevance Measures on the Robustness and Accuracy of Collaborative Filtering
The open nature of collaborative recommender systems present a security problem. Attackers that cannot be readily distinguished from ordinary users may inject biased profiles, deg...
Jeff J. Sandvig, Bamshad Mobasher, Robin D. Burke
WEBI
2009
Springer
14 years 2 months ago
Zero-Sum Reward and Punishment Collaborative Filtering Recommendation Algorithm
In this paper, we propose a novel memory-based collaborative filtering recommendation algorithm. Our algorithm use a new metric named influence weight, which is adjusted with ze...
Nan Li, Chunping Li
KDD
2006
ACM
172views Data Mining» more  KDD 2006»
14 years 8 months ago
Attack detection in time series for recommender systems
Recent research has identified significant vulnerabilities in recommender systems. Shilling attacks, in which attackers introduce biased ratings in order to influence future recom...
Sheng Zhang, Amit Chakrabarti, James Ford, Fillia ...