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» Ranking List Dispersion as a Query Performance Predictor
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MM
2005
ACM
172views Multimedia» more  MM 2005»
14 years 1 months ago
Learning the semantics of multimedia queries and concepts from a small number of examples
In this paper we unify two supposedly distinct tasks in multimedia retrieval. One task involves answering queries with a few examples. The other involves learning models for seman...
Apostol Natsev, Milind R. Naphade, Jelena Tesic
IPM
2008
100views more  IPM 2008»
13 years 7 months ago
Query-level loss functions for information retrieval
Many machine learning technologies such as support vector machines, boosting, and neural networks have been applied to the ranking problem in information retrieval. However, since...
Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng W...
WWW
2010
ACM
14 years 2 months ago
Actively predicting diverse search intent from user browsing behaviors
This paper is concerned with actively predicting search intent from user browsing behavior data. In recent years, great attention has been paid to predicting user search intent. H...
Zhicong Cheng, Bin Gao, Tie-Yan Liu
WSDM
2010
ACM
245views Data Mining» more  WSDM 2010»
14 years 5 months ago
Improving Quality of Training Data for Learning to Rank Using Click-Through Data
In information retrieval, relevance of documents with respect to queries is usually judged by humans, and used in evaluation and/or learning of ranking functions. Previous work ha...
Jingfang Xu, Chuanliang Chen, Gu Xu, Hang Li, Elbi...
SIGIR
2004
ACM
14 years 1 months ago
Evaluating high accuracy retrieval techniques
Although information retrieval research has always been concerned with improving the effectiveness of search, in some applications, such as information analysis, a more specific ...
Chirag Shah, W. Bruce Croft