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GPEM
2006
117views more  GPEM 2006»
14 years 11 months ago
Introducing lateral thinking in search engines
Abstract. Too much information kills information. This common statement applies to huge databases, where state of the art search engines may retrieve hundreds of very similar docum...
Yann Landrin-Schweitzer, Pierre Collet, Evelyne Lu...
SIGIR
2009
ACM
15 years 6 months ago
A bayesian learning approach to promoting diversity in ranking for biomedical information retrieval
In this paper, we propose a Bayesian learning approach to promoting diversity for information retrieval in biomedicine and a re-ranking model to improve retrieval performance in t...
Xiangji Huang, Qinmin Hu
ISMIR
2004
Springer
87views Music» more  ISMIR 2004»
15 years 5 months ago
Search Effectiveness Measures for Symbolic Music Queries in Very Large Databases
In the interest of establishing robust benchmarks for search efficiency, we conducted a series of tests on symbolic databases of musical incipits and themes taken from several di...
Craig Stuart Sapp, Yi-Wen Liu, Eleanor Selfridge-F...
AIRS
2006
Springer
15 years 3 months ago
Improving Re-ranking of Search Results Using Collaborative Filtering
Search Engines today often return a large volume of results with possibly a few relevant results. The notion of relevance is subjective and depends on the user and the context of ...
U. Rohini, Vamshi Ambati
WWW
2004
ACM
16 years 14 days ago
A hierarchical monothetic document clustering algorithm for summarization and browsing search results
Organizing Web search results into a hierarchy of topics and subtopics facilitates browsing the collection and locating results of interest. In this paper, we propose a new hierar...
Krishna Kummamuru, Rohit Lotlikar, Shourya Roy, Ka...