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KDD
2007
ACM
192views Data Mining» more  KDD 2007»
14 years 8 months ago
Active exploration for learning rankings from clickthrough data
We address the task of learning rankings of documents from search engine logs of user behavior. Previous work on this problem has relied on passively collected clickthrough data. ...
Filip Radlinski, Thorsten Joachims
EWCBR
2004
Springer
14 years 1 months ago
Improving Recommendation Ranking by Learning Personal Feature Weights
The ranking of offers is an issue in e-commerce that has received a lot of attention in Case-Based Reasoning research. In the absence of a sales assistant, it is important to provi...
Lorcan Coyle, Padraig Cunningham
AAAI
2011
12 years 7 months ago
CCRank: Parallel Learning to Rank with Cooperative Coevolution
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coev...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...
MIR
2010
ACM
207views Multimedia» more  MIR 2010»
13 years 6 months ago
Learning to rank for content-based image retrieval
In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking st...
Fabio F. Faria, Adriano Veloso, Humberto Mossri de...
KDD
2009
ACM
192views Data Mining» more  KDD 2009»
14 years 8 months ago
Learning optimal ranking with tensor factorization for tag recommendation
Tag recommendation is the task of predicting a personalized list of tags for a user given an item. This is important for many websites with tagging capabilities like last.fm or de...
Steffen Rendle, Leandro Balby Marinho, Alexandros ...