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IIR
2010
13 years 11 months ago
Context-Dependent Recommendations with Items Splitting
Recommender systems are intelligent applications that help on-line users to tackle information overload by providing recommendations of relevant items. Collaborative Filtering (CF...
Linas Baltrunas, Francesco Ricci
STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 11 months ago
Probabilistic Association Rules for Item-Based Recommender Systems
Since the beginning of the 1990's, the Internet has constantly grown, proposing more and more services and sources of information. The challenge is no longer to provide users ...
Sylvain Castagnos, Armelle Brun, Anne Boyer
RTAS
2005
IEEE
14 years 3 months ago
Feedback-Based Dynamic Voltage and Frequency Scaling for Memory-Bound Real-Time Applications
Dynamic voltage and frequency scaling is increasingly being used to reduce the energy requirements of embedded and real-time applications by exploiting idle CPU resources, while s...
Christian Poellabauer, Leo Singleton, Karsten Schw...
RECSYS
2009
ACM
14 years 4 months ago
TagiCoFi: tag informed collaborative filtering
Besides the rating information, an increasing number of modern recommender systems also allow the users to add personalized tags to the items. Such tagging information may provide...
Yi Zhen, Wu-Jun Li, Dit-Yan Yeung
RECSYS
2009
ACM
14 years 4 months ago
A partial-order based active cache for recommender systems
Recommender systems aim to substantially reduce information overload by suggesting lists of similar items that users may find interesting. Caching has been a useful technique for...
Umar Qasim, Vincent Oria, Yi-fang Brook Wu, Michae...