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183
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ECWEB
2011
Springer
233views ECommerce» more  ECWEB 2011»
13 years 10 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
100
Voted
COOPIS
2004
IEEE
15 years 2 months ago
Trust-Aware Collaborative Filtering for Recommender Systems
Recommender Systems allow people to find the resources they need by making use of the experiences and opinions of their nearest neighbours. Costly annotations by experts are replac...
Paolo Massa, Paolo Avesani
76
Voted
WSDM
2012
ACM
246views Data Mining» more  WSDM 2012»
13 years 5 months ago
Auralist: introducing serendipity into music recommendation
Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, produc...
Yuan Cao Zhang, Diarmuid Ó Séaghdha,...
124
Voted
JMLR
2012
13 years 21 days ago
Multiple Texture Boltzmann Machines
We assess the generative power of the mPoTmodel of [10] with tiled-convolutional weight sharing as a model for visual textures by specifically training on this task, evaluating m...
Jyri J. Kivinen, Christopher K. I. Williams
INFOCOM
2012
IEEE
13 years 21 days ago
Fine-grained private matching for proximity-based mobile social networking
—Proximity-based mobile social networking (PMSN) refers to the social interaction among physically proximate mobile users directly through the Bluetooth/WiFi interfaces on their ...
Rui Zhang 0007, Yanchao Zhang, Jinyuan Sun, Guanhu...