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» It takes variety to make a world: diversification in recomme...
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EDBT
2009
ACM
108views Database» more  EDBT 2009»
13 years 8 months ago
It takes variety to make a world: diversification in recommender systems
Cong Yu, Laks V. S. Lakshmanan, Sihem Amer-Yahia
WWW
2008
ACM
14 years 5 months ago
Video suggestion and discovery for youtube: taking random walks through the view graph
The rapid growth of the number of videos in YouTube provides enormous potential for users to find content of interest to them. Unfortunately, given the difficulty of searching vid...
Shumeet Baluja, Rohan Seth, D. Sivakumar, Yushi Ji...
SIGIR
2011
ACM
12 years 7 months ago
Fast context-aware recommendations with factorization machines
The situation in which a choice is made is an important information for recommender systems. Context-aware recommenders take this information into account to make predictions. So ...
Steffen Rendle, Zeno Gantner, Christoph Freudentha...
SAC
2005
ACM
13 years 10 months ago
A trust-enhanced recommender system application: Moleskiing
Recommender Systems (RS) suggests to users items they will like based on their past opinions. Collaborative Filtering (CF) is the most used technique to assess user similarity bet...
Paolo Avesani, Paolo Massa, Roberto Tiella
SDM
2010
SIAM
200views Data Mining» more  SDM 2010»
13 years 6 months ago
Residual Bayesian Co-clustering for Matrix Approximation
In recent years, matrix approximation for missing value prediction has emerged as an important problem in a variety of domains such as recommendation systems, e-commerce and onlin...
Hanhuai Shan, Arindam Banerjee