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CAI
2006
Springer
14 years 9 months ago
A Logical Framework for Identifying and Explaining Unexpected News
The number of news reports published online is now so great that it is impossible for any person to read all of them. Not all of these reports are equally interesting. Automating ...
Emma Byrne
CISS
2008
IEEE
15 years 4 months ago
A lower-bound on the number of rankings required in recommender systems using collaborativ filtering
— We consider the situation where users rank items from a given set, and each user ranks only a (small) subset of all items. We assume that users can be classified into C classe...
Peter Marbach
RIAO
1997
14 years 11 months ago
The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web
The Web is a vast, dynamic source of information and resources. Because of its size and diversity, it is increasingly likely that if the information one seeks is not already there...
Mark S. Ackerman, Brian Starr, Michael J. Pazzani
KDD
2006
ACM
170views Data Mining» more  KDD 2006»
15 years 10 months ago
Classification features for attack detection in collaborative recommender systems
Collaborative recommender systems are highly vulnerable to attack. Attackers can use automated means to inject a large number of biased profiles into such a system, resulting in r...
Robin D. Burke, Bamshad Mobasher, Chad Williams, R...
KDD
2009
ACM
162views Data Mining» more  KDD 2009»
15 years 10 months ago
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Collaborative filtering is the most popular approach to build recommender systems and has been successfully employed in many applications. However, it cannot make recommendations ...
Mohsen Jamali, Martin Ester