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CIKM
2009
Springer
14 years 2 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
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...
VLDB
2007
ACM
132views Database» more  VLDB 2007»
14 years 1 months ago
EntityRank: Searching Entities Directly and Holistically
As the Web has evolved into a data-rich repository, with the standard “page view,” current search engines are becoming increasingly inadequate for a wide range of query tasks....
Tao Cheng, Xifeng Yan, Kevin Chen-Chuan Chang
SIGMOD
2008
ACM
206views Database» more  SIGMOD 2008»
14 years 7 months ago
Ad-hoc aggregations of ranked lists in the presence of hierarchies
A variety of web sites and web based services produce textual lists at varying time granularities ranked according to several criteria. For example, Google Trends produces lists o...
Nilesh Bansal, Sudipto Guha, Nick Koudas
EDBT
2009
ACM
208views Database» more  EDBT 2009»
14 years 2 months ago
Flexible and efficient querying and ranking on hyperlinked data sources
There has been an explosion of hyperlinked data in many domains, e.g., the biological Web. Expressive query languages and effective ranking techniques are required to convert this...
Ramakrishna Varadarajan, Vagelis Hristidis, Louiqa...