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AUSDM
2007
Springer
107views Data Mining» more  AUSDM 2007»
13 years 11 months ago
Preference Networks: Probabilistic Models for Recommendation Systems
Recommender systems are important to help users select relevant and personalised information over massive amounts of data available. We propose an unified framework called Prefer...
Tran The Truyen, Dinh Q. Phung, Svetha Venkatesh
UIC
2007
Springer
13 years 11 months ago
Location-Based Recommendation System Using Bayesian User's Preference Model in Mobile Devices
As wireless communication advances, research on location-based services using mobile devices has attracted interest, which provides information and services related to user’s phy...
Moon-Hee Park, Jin-Hyuk Hong, Sung-Bae Cho
AIA
2007
13 years 6 months ago
Adaptive preference elicitation for top-K recommendation tasks using GAI-networks
The enormous number of questions needed to acquire a full preference model when the size of the outcome space is large forces us to work with partial models that approximate the u...
Sérgio R. de M. Queiroz
ECAI
2008
Springer
13 years 6 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
STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 6 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