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» Search Engines that Learn from Implicit Feedback
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SIGIR
2005
ACM
15 years 4 months ago
Combining eye movements and collaborative filtering for proactive information retrieval
We study a new task, proactive information retrieval by combining implicit relevance feedback and collaborative filtering. We have constructed a controlled experimental setting, ...
Kai Puolamäki, Jarkko Salojärvi, Eerika ...
SAINT
2006
IEEE
15 years 5 months ago
MAAY: A Decentralized Personalized Search System
This paper focuses on decentralized personalized search engines. It is composed of three parts. Firstly, we formulate the problem and we propose a graph-based measure of quality o...
Frederic Dang Ngoc, Joaquín Keller, Gwendal...
KES
2010
Springer
14 years 9 months ago
DOCODE-Lite: A Meta-Search Engine for Document Similarity Retrieval
The retrieval of similar documents from large scale datasets has been the one of the main concerns in knowledge management environments, such as plagiarism detection, news impact a...
Felipe Bravo-Marquez, Gaston L'Huillier, Sebasti&a...
IUI
2005
ACM
15 years 4 months ago
Active preference learning for personalized calendar scheduling assistance
We present PLIANT, a learning system that supports adaptive assistance in an open calendaring system. PLIANT learns user preferences from the feedback that naturally occurs during...
Melinda T. Gervasio, Michael D. Moffitt, Martha E....
KES
2005
Springer
15 years 4 months ago
User Preference Learning for Multimedia Personalization in Pervasive Computing Environment
Pervasive computing environment and users’ demand for multimedia personalization precipitate a need for personalization tools to help people access desired multimedia content at ...
Zhiwen Yu, Daqing Zhang, Xingshe Zhou, Changde Li