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» Search Engines that Learn from Implicit Feedback
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CIKM
2009
Springer
15 years 2 months ago
Learning from past queries for resource selection
Federated text search provides a unified search interface for multiple search engines of distributed text information sources. Resource selection is an important component for fed...
Suleyman Cetintas, Luo Si, Hao Yuan
104
Voted
CIKM
2009
Springer
15 years 6 days ago
Improving search engines using human computation games
Work on evaluating and improving the relevance of web search engines typically use human relevance judgments or clickthrough data. Both these methods look at the problem of learni...
Hao Ma, Raman Chandrasekar, Chris Quirk, Abhishek ...
PKDD
2010
Springer
168views Data Mining» more  PKDD 2010»
14 years 9 months ago
Bayesian Knowledge Corroboration with Logical Rules and User Feedback
Current knowledge bases suffer from either low coverage or low accuracy. The underlying hypothesis of this work is that user feedback can greatly improve the quality of automatica...
Gjergji Kasneci, Jurgen Van Gael, Ralf Herbrich, T...
SIGIR
2012
ACM
13 years 1 months ago
TFMAP: optimizing MAP for top-n context-aware recommendation
In this paper, we tackle the problem of top-N context-aware recommendation for implicit feedback scenarios. We frame this challenge as a ranking problem in collaborative filterin...
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas, ...
87
Voted
ATAL
2004
Springer
15 years 4 months ago
QueryTracker: An Agent for Tracking Persistent Information Needs
Most people have long term information interests. Current Web search engines satisfy immediate information needs. Specific sites support tracking of long term interests. We prese...
Gabriel Somlo, Adele E. Howe