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» Comparing relevance feedback algorithms for web search
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CIKM
2008
Springer
13 years 9 months ago
Active relevance feedback for difficult queries
Relevance feedback has been demonstrated to be an effective strategy for improving retrieval accuracy. The existing relevance feedback algorithms based on language models and vect...
Zuobing Xu, Ram Akella
CIKM
2009
Springer
14 years 2 months ago
Web search result summarization: title selection algorithms and user satisfaction
Eye tracking experiments have shown that titles of Web search results play a crucial role in guiding a user’s search process. We present a machine-learned algorithm that trains ...
Tapas Kanungo, Nadia Ghamrawi, Ki Yuen Kim, Lawren...
SIGIR
2011
ACM
12 years 10 months ago
A boosting approach to improving pseudo-relevance feedback
Pseudo-relevance feedback has proven effective for improving the average retrieval performance. Unfortunately, many experiments have shown that although pseudo-relevance feedback...
Yuanhua Lv, ChengXiang Zhai, Wan Chen
AI
2008
Springer
14 years 2 months 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...
SEMWEB
2009
Springer
14 years 2 months ago
Exploiting User Feedback to Improve Semantic Web Service Discovery
State-of-the-art discovery of Semantic Web services is based on hybrid algorithms that combine semantic and syntactic matchmaking. These approaches are purely based on similarity m...
Anna Averbakh, Daniel Krause, Dimitrios Skoutas