43 search results - page 5 / 9 » Query chains: learning to rank from implicit feedback |
170
Voted
CVPR
2007
IEEE
15 years 7 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...
144
Voted
CIKM
2011
Springer
14 years 3 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...
142
Voted
KDD
2009
ACM
15 years 8 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...
140
Voted
CIKM
2008
Springer
15 years 5 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...
126
Voted
WWW
2009
ACM
16 years 4 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...