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» Accuracy in Rating and Recommending Item Features
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AH
2008
Springer
13 years 11 months ago
Accuracy in Rating and Recommending Item Features
This paper discusses accuracy in processing ratings of and recommendations for item features. Such processing facilitates featurebased user navigation in recommender system interfa...
Lloyd Rutledge, Natalia Stash, Yiwen Wang, Lora Ar...
ICDM
2010
IEEE
172views Data Mining» more  ICDM 2010»
13 years 2 months ago
Learning Attribute-to-Feature Mappings for Cold-Start Recommendations
Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such ca...
Zeno Gantner, Lucas Drumond, Christoph Freudenthal...
RECSYS
2009
ACM
13 years 11 months ago
Rate it again: increasing recommendation accuracy by user re-rating
A common approach to designing Recommender Systems (RS) consists of asking users to explicitly rate items in order to collect feedback about their preferences. However, users have...
Xavier Amatriain, Josep M. Pujol, Nava Tintarev, N...
CIKM
2009
Springer
13 years 11 months ago
Hydra: a hybrid recommender system [cross-linked rating and content information]
This paper discusses the combination of collaborative and contentbased filtering in the context of web-based recommender systems. In particular, we link the well-known MovieLens ...
Stephan Spiegel, Jérôme Kunegis, Fang...
CIA
2004
Springer
13 years 10 months ago
Qualitative Analysis of User-Based and Item-Based Prediction Algorithms for Recommendation Agents
Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, several prediction algorithms are described and evaluated...
Manos Papagelis, Dimitris Plexousakis