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» Directly optimizing evaluation measures in learning to rank
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IPM
2008
100views more  IPM 2008»
14 years 9 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...
TREC
2003
14 years 11 months ago
Ranking Function Discovery by Genetic Programming for Robust Retrieval
Ranking functions are instrumental for the success of an information retrieval (search engine) system. However nearly all existing ranking functions are manually designed based on...
Li Wang, Weiguo Fan, Rui Yang, Wensi Xi, Ming Luo,...
SIGIR
2005
ACM
15 years 3 months ago
Linear discriminant model for information retrieval
This paper presents a new discriminative model for information retrieval (IR), referred to as linear discriminant model (LDM), which provides a flexible framework to incorporate a...
Jianfeng Gao, Haoliang Qi, Xinsong Xia, Jian-Yun N...
SIGMOD
2004
ACM
163views Database» more  SIGMOD 2004»
15 years 9 months ago
Rank-aware Query Optimization
Ranking is an important property that needs to be fully supported by current relational query engines. Recently, several rank-join query operators have been proposed based on rank...
Ihab F. Ilyas, Rahul Shah, Walid G. Aref, Jeffrey ...
108
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WSDM
2012
ACM
285views Data Mining» more  WSDM 2012»
13 years 5 months ago
Probabilistic models for personalizing web search
We present a new approach for personalizing Web search results to a specific user. Ranking functions for Web search engines are typically trained by machine learning algorithms u...
David Sontag, Kevyn Collins-Thompson, Paul N. Benn...