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KDD
2005
ACM
177views Data Mining» more  KDD 2005»
14 years 5 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
ICMCS
2006
IEEE
144views Multimedia» more  ICMCS 2006»
13 years 10 months ago
Using Implicit Relevane Feedback to Advance Web Image Search
Although relevance feedback has been extensively studied in content-based image retrieval in the academic area, no commercial web image search engine has employed the idea. There ...
En Cheng, Feng Jing, Mingjing Li, Wei-Ying Ma, Hai...
ICPR
2008
IEEE
13 years 11 months ago
Re-ranking of web image search results using a graph algorithm
We propose a method to improve the results of image search engines on the Internet to satisfy users who desire to see relevant images in the first few pages. The method re-ranks ...
Hilal Zitouni, Sare Gul Sevil, Derya Ozkan, Pinar ...
WSDM
2009
ACM
115views Data Mining» more  WSDM 2009»
13 years 11 months ago
Discovering and using groups to improve personalized search
Personalized Web search takes advantage of information about an individual to identify the most relevant results for that person. A challenge for personalization lies in collectin...
Jaime Teevan, Meredith Ringel Morris, Steve Bush
CHI
2007
ACM
14 years 5 months ago
IGroup: presenting web image search results in semantic clusters
Current web image search engines still rely on user typing textual description: query word(s) for visual targets. As the queries are often short, general or even ambiguous, the im...
Shuo Wang, Feng Jing, Jibo He, Qixing Du, Lei Zhan...