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SIGIR
2006
ACM
14 years 1 months ago
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
Memory-based methods for collaborative filtering predict new ratings by averaging (weighted) ratings between, respectively, pairs of similar users or items. In practice, a large ...
Jun Wang, Arjen P. de Vries, Marcel J. T. Reinders
ICDM
2008
IEEE
183views Data Mining» more  ICDM 2008»
14 years 1 months ago
Collaborative Filtering for Implicit Feedback Datasets
A common task of recommender systems is to improve customer experience through personalized recommendations based on prior implicit feedback. These systems passively track differe...
Yifan Hu, Yehuda Koren, Chris Volinsky
ECAI
2008
Springer
13 years 9 months ago
Probabilistic Reinforcement Rules for Item-Based Recommender Systems
The Internet is constantly growing, proposing more and more services and sources of information. Modeling personal preferences enables recommender systems to identify relevant subs...
Sylvain Castagnos, Armelle Brun, Anne Boyer
ECWEB
2011
Springer
233views ECommerce» more  ECWEB 2011»
12 years 7 months ago
Rating Elicitation Strategies for Collaborative Filtering
The accuracy of collaborative filtering recommender systems largely depends on two factors: the quality of the recommendation algorithm and the nature of the available item rating...
Mehdi Elahi, Valdemaras Repsys, Francesco Ricci
IUI
2010
ACM
14 years 20 days ago
Towards a reputation-based model of social web search
While web search tasks are often inherently collaborative in nature, many search engines do not explicitly support collaboration during search. In this paper, we describe HeyStaks...
Kevin KcNally, Michael P. O'Mahony, Barry Smyth, M...