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» Learning to rank with partially-labeled data
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WWW
2005
ACM
16 years 2 months ago
Adaptive page ranking with neural networks
Recent developments in the area of neural networks provided new models which are capable of processing general types of graph structures. Neural networks are well-known for their ...
Franco Scarselli, Sweah Liang Yong, Markus Hagenbu...
WSDM
2012
ACM
285views Data Mining» more  WSDM 2012»
13 years 9 months ago
Probabilistic models for personalizing web search
We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms u...
David Sontag, Kevyn Collins-Thompson, Paul N. Benn...
ECIR
2009
Springer
15 years 11 months ago
Combination of Documents Features Based on Simulated Click-through Data
Many different ranking algorithms based on content and context have been used in web search engines to find pages based on a user query. Furthermore, to achieve better performance ...
Ali Mohammad Zareh Bidoki, James A. Thom
KDD
2009
ACM
184views Data Mining» more  KDD 2009»
15 years 8 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...
WSDM
2010
ACM
210views Data Mining» more  WSDM 2010»
15 years 11 months ago
Towards Recency Ranking in Web Search
In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detect...
Anlei Dong, Yi Chang, Zhaohui Zheng, Gilad Mishne,...