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WWW
2008
ACM
14 years 8 months ago
Learning to rank relational objects and its application to web search
Learning to rank is a new statistical learning technology on creating a ranking model for sorting objects. The technology has been successfully applied to web search, and is becom...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...
IRI
2007
IEEE
14 years 2 months ago
Acronym-Expansion Recognition and Ranking on the Web
The paper presents a study on large-scale automatic extraction of acronyms and associated expansions from Web data and from the user interactions with this data through Web search...
Alpa Jain, Silviu Cucerzan, Saliha Azzam
SIGIR
2009
ACM
14 years 2 months ago
Global ranking by exploiting user clicks
It is now widely recognized that user interactions with search results can provide substantial relevance information on the documents displayed in the search results. In this pape...
Shihao Ji, Ke Zhou, Ciya Liao, Zhaohui Zheng, Gui-...
WEBI
2007
Springer
14 years 1 months ago
Experimental Bounds on the Usefulness of Personalized and Topic-Sensitive PageRank
PageRank is an algorithm used by several search engines to rank web documents according to their assumed relevance and popularity deduced from the Web’s link structure. PageRank...
Sinan Al-Saffar, Gregory L. Heileman
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...