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WECWIS
2009
IEEE
159views ECommerce» more  WECWIS 2009»
14 years 2 months ago
Context-Aware Recommendation by Aggregating User Context
— Traditional recommendation approaches do not consider the changes of user preferences according to context. As a result, these approaches consider the user’s overall preferen...
Dongmin Shin, Jae-won Lee, Jongheum Yeon, Sang-goo...
STAIRS
2008
169views Education» more  STAIRS 2008»
13 years 8 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
AI
2009
Springer
14 years 2 months ago
Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model
Abstract. We use a hierarchical Bayesian approach to model user preferences in different contexts or settings. Unlike many previous recommenders, our approach is content-based. We...
Daniel Pomerantz, Gregory Dudek
DSRT
2005
IEEE
14 years 1 months ago
Collaborative Visualization: A Review and Taxonomy
We present a brief review of 42 collaborative visualization systems, grouped into four application areas: collaborative problem-solving environments, virtual reality environments,...
Ian J. Grimstead, David W. Walker, Nick J. Avis
KDD
2008
ACM
155views Data Mining» more  KDD 2008»
14 years 7 months ago
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are ana...
Yehuda Koren