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» Ranking List Dispersion as a Query Performance Predictor
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SIGIR
2008
ACM
13 years 7 months ago
Learning to rank at query-time using association rules
Some applications have to present their results in the form of ranked lists. This is the case of many information retrieval applications, in which documents must be sorted accordi...
Adriano Veloso, Humberto Mossri de Almeida, Marcos...
WSDM
2012
ACM
267views Data Mining» more  WSDM 2012»
12 years 3 months ago
Learning to rank with multi-aspect relevance for vertical search
Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-d...
Changsung Kang, Xuanhui Wang, Yi Chang, Belle L. T...
ICASSP
2008
IEEE
14 years 2 months ago
Query by humming of midi and audio using locality sensitive hashing
This paper proposes a query by humming method based on locality sensitive hashing (LSH). The method constructs an index of melodic fragments by extracting pitch vectors from a dat...
Matti Ryynänen, Anssi Klapuri
BMCBI
2010
110views more  BMCBI 2010»
13 years 8 months ago
Concept-based query expansion for retrieving gene related publications from MEDLINE
Background: Advances in biotechnology and in high-throughput methods for gene analysis have contributed to an exponential increase in the number of scientific publications in thes...
Sérgio Matos, Joel Arrais, João Maia...
TKDE
2008
191views more  TKDE 2008»
13 years 7 months ago
Beyond Single-Page Web Search Results
Given a user keyword query, current Web search engines return a list of individual Web pages ranked by their "goodness" with respect to the query. Thus, the basic unit fo...
Ramakrishna Varadarajan, Vagelis Hristidis, Tao Li