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» Learning to rank on graphs
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KDD
2009
ACM
184views Data Mining» more  KDD 2009»
16 years 1 months ago
Thumbs-Up: a game for playing to rank search results
Human computation is an effective way to channel human effort spent playing games to solving computational problems that are easy for humans but difficult for computers to autom...
Ali Dasdan, Chris Drome, Santanu Kolay, Micah Alpe...
WSDM
2010
ACM
211views Data Mining» more  WSDM 2010»
15 years 11 months ago
IntervalRank - Isotonic Regression with Listwise and Pairwise Constraints
Ranking a set of retrieved documents according to their relevance to a given query has become a popular problem at the intersection of web search, machine learning, and informatio...
Taesup Moon, Alex Smola, Yi Chang, Zhaohui Zheng
ICML
2006
IEEE
16 years 7 months ago
Graph model selection using maximum likelihood
In recent years, there has been a proliferation of theoretical graph models, e.g., preferential attachment and small-world models, motivated by real-world graphs such as the Inter...
Adam Kalai, Ivona Bezáková, Rahul Sa...
WSDM
2010
ACM
210views Data Mining» more  WSDM 2010»
16 years 3 months ago
Towards Recency Ranking in Web Search
In web search, recency ranking refers to ranking documents by relevance which takes freshness into account. In this paper, we propose a retrieval system which automatically detect...
Anlei Dong, Yi Chang, Zhaohui Zheng, Gilad Mishne,...
DIS
2006
Springer
15 years 10 months ago
Optimal Bayesian 2D-Discretization for Variable Ranking in Regression
In supervised machine learning, variable ranking aims at sorting the input variables according to their relevance w.r.t. an output variable. In this paper, we propose a new relevan...
Marc Boullé, Carine Hue