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» Search Engines that Learn from Implicit Feedback
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CVPR
2007
IEEE
15 years 10 months ago
Diverse Active Ranking for Multimedia Search
Interactively learning from a small sample of unlabeled examples is an enormously challenging task, one that often arises in vision applications. Relevance feedback and more recen...
ShyamSundar Rajaram, Charlie K. Dagli, Nemanja Pet...
ICADL
2010
Springer
160views Education» more  ICADL 2010»
15 years 11 months ago
Thesaurus Extension Using Web Search Engines
Maintaining and extending large thesauri is an important challenge facing digital libraries and IT businesses alike. In this paper we describe a method building on and extending ex...
Robert Meusel, Mathias Niepert, Kai Eckert, Heiner...
CEAS
2005
Springer
15 years 11 months ago
Implicit Queries for Email
Implicit query systems examine a document and automatically conduct searches for the most relevant information. In this paper, we offer three contributions to implicit query resea...
Joshua Goodman, Vitor R. Carvalho
WWW
2006
ACM
16 years 6 months ago
Using annotations in enterprise search
A major difference between corporate intranets and the Internet is that in intranets the barrier for users to create web pages is much higher. This limits the amount and quality o...
Pavel A. Dmitriev, Nadav Eiron, Marcus Fontoura, E...
AI
2008
Springer
16 years 15 days ago
A Frequency Mining-Based Algorithm for Re-ranking Web Search Engine Retrievals
Abstract. Conventional web search engines retrieve too many documents for the majority of the submitted queries; therefore, they possess a good recall, since there are far more pag...
M. Barouni-Ebrahimi, Ebrahim Bagheri, Ali A. Ghorb...