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» Using historical data to enhance rank aggregation
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CIKM
2008
Springer
15 years 1 months ago
Are click-through data adequate for learning web search rankings?
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...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
WSDM
2012
ACM
267views Data Mining» more  WSDM 2012»
13 years 7 months ago
Learning to rank with multi-aspect relevance for vertical search
Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-d...
Changsung Kang, Xuanhui Wang, Yi Chang, Belle L. T...
KDD
2009
ACM
184views Data Mining» more  KDD 2009»
15 years 6 months ago
Thumbs-Up: a game for playing to rank search results
Human computation is an effective way to channel human effort spent playing games to solving computational problems that are easy for humans but difficult for computers to autom...
Ali Dasdan, Chris Drome, Santanu Kolay, Micah Alpe...
EDBT
2009
ACM
208views Database» more  EDBT 2009»
15 years 6 months ago
Flexible and efficient querying and ranking on hyperlinked data sources
There has been an explosion of hyperlinked data in many domains, e.g., the biological Web. Expressive query languages and effective ranking techniques are required to convert this...
Ramakrishna Varadarajan, Vagelis Hristidis, Louiqa...
JDM
2007
56views more  JDM 2007»
14 years 11 months ago
Temporal Aggregation Using a Multidimensional Index
We present a new method for computing temporal aggregation that uses a multidimensional index. The novelty of our method lies in mapping the start time and end time of a temporal ...
Joon-Ho Woo, Byung Suk Lee, Min-Jae Lee, Woong-Kee...