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KDD
2008
ACM
147views Data Mining» more  KDD 2008»
14 years 8 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
XSYM
2004
Springer
115views Database» more  XSYM 2004»
14 years 28 days ago
XQuery Processing with Relevance Ranking
Abstract. We are presenting a coherent framework for XQuery processing that incorporates IR-style approximate matching and allows the ordering of results by their relevance score. ...
Leonidas Fegaras
WWW
2007
ACM
14 years 8 months ago
Deriving knowledge from figures for digital libraries
Figures in digital documents contain important information. Current digital libraries do not summarize and index information available within figures for document retrieval. We pr...
Xiaonan Lu, James Ze Wang, Prasenjit Mitra, C. Lee...
GECCO
2009
Springer
151views Optimization» more  GECCO 2009»
14 years 2 months ago
Swarming to rank for information retrieval
This paper presents an approach to automatically optimize the retrieval quality of ranking functions. Taking a Swarm Intelligence perspective, we present a novel method, SwarmRank...
Ernesto Diaz-Aviles, Wolfgang Nejdl, Lars Schmidt-...
SIGIR
2010
ACM
13 years 7 months ago
Learning to rank query reformulations
Query reformulation techniques based on query logs have recently proven to be effective for web queries. However, when initial queries have reasonably good quality, these techniqu...
Van Dang, Michael Bendersky, W. Bruce Croft