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» Comparing relevance feedback algorithms for web search
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KDD
2005
ACM
177views Data Mining» more  KDD 2005»
14 years 8 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
CIKM
2009
Springer
14 years 11 days ago
A comparative study of methods for estimating query language models with pseudo feedback
We systematically compare five representative state-of-theart methods for estimating query language models with pseudo feedback in ad hoc information retrieval, including two var...
Yuanhua Lv, ChengXiang Zhai
WISE
2000
Springer
14 years 3 days ago
WebSail: From On-Line Learning to Web Search
In this paper we report our research on building WebSail { an intelligent web search engine that is able to perform real-time adaptive learning. WebSail learns from the user'...
Zhixiang Chen, Xiannong Meng, Binhai Zhu, Richard ...
SIGIR
2008
ACM
13 years 7 months ago
A study of methods for negative relevance feedback
Negative relevance feedback is a special case of relevance feedback where we do not have any positive example; this often happens when the topic is difficult and the search result...
Xuanhui Wang, Hui Fang, ChengXiang Zhai
WWW
2008
ACM
14 years 8 months ago
Mining the search trails of surfing crowds: identifying relevant websites from user activity
The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users. While it has been previously shown that...
Mikhail Bilenko, Ryen W. White