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CIKM
2008
Springer
15 years 9 months ago
Are click-through data adequate for learning web search rankings?
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
IJCAI
2007
15 years 9 months ago
Web Page Clustering Using Heuristic Search in the Web Graph
Effective representation of Web search results remains an open problem in the Information Retrieval community. For ambiguous queries, a traditional approach is to organize search ...
Ron Bekkerman, Shlomo Zilberstein, James Allan
CIKM
2011
Springer
14 years 7 months ago
Personalizing web search results by reading level
Traditionally, search engines have ignored the reading difficulty of documents and the reading proficiency of users in computing a document ranking. This is one reason why Web se...
Kevyn Collins-Thompson, Paul N. Bennett, Ryen W. W...
WSDM
2012
ACM
267views Data Mining» more  WSDM 2012»
14 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...
WWW
2004
ACM
16 years 8 months ago
Query and content suggestion based on latent interest and topic class
To improve the process of user information retrieval, we propose the concept of a latent semantic map (LSM), along with a method of generating this map. The novel aspect of the LS...
Noriaki Kawaeme, Hideaki Suzuki, Osamu Mizuno