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» Adaptive relevance feedback in information retrieval
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KDD
2006
ACM
134views Data Mining» more  KDD 2006»
14 years 9 months ago
Learning to rank networked entities
Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections...
Alekh Agarwal, Soumen Chakrabarti, Sunny Aggarwal
SIGMOD
2010
ACM
231views Database» more  SIGMOD 2010»
14 years 1 months ago
Automatically incorporating new sources in keyword search-based data integration
Scientific data offers some of the most interesting challenges in data integration today. Scientific fields evolve rapidly and accumulate masses of observational and experiment...
Partha Pratim Talukdar, Zachary G. Ives, Fernando ...
SIGIR
2004
ACM
14 years 2 months ago
Display time as implicit feedback: understanding task effects
Recent research has had some success using the length of time a user displays a document in their web browser as implicit feedback for document preference. However, most studies h...
Diane Kelly, Nicholas J. Belkin
DEXAW
2009
IEEE
139views Database» more  DEXAW 2009»
14 years 3 months ago
Improving Web Page Retrieval Using Search Context from Clicked Domain Names
Abstract—Search context is a crucial factor that helps to understand a user’s information need in ad-hoc Web page retrieval. A query log of a search engine contains rich inform...
Rongmei Li
KDD
2005
ACM
177views Data Mining» more  KDD 2005»
14 years 9 months ago
Query chains: learning to rank from implicit feedback
This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform ...
Filip Radlinski, Thorsten Joachims