43 search results - page 5 / 9 » Query chains: learning to rank from implicit feedback |
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CVPR
2007
IEEE
15 years 1 months ago
2007
IEEE
Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recen...
CIKM
2011
Springer
13 years 9 months ago
2011
Springer
Evaluating rankers using implicit feedback, such as clicks on documents in a result list, is an increasingly popular alternative to traditional evaluation methods based on explici...
KDD
2009
ACM
15 years 2 months ago
2009
ACM
1 In this article, we report our efforts in mining the information encoded as clickthrough data in the server logs to evaluate and monitor the relevance ranking quality of a commer...
CIKM
2008
Springer
14 years 11 months ago
2008
Springer
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
WWW
2009
ACM
15 years 10 months ago
2009
ACM
As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can...