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ICML
2005
IEEE
16 years 6 months ago
Learning to rank using gradient descent
We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these...
Christopher J. C. Burges, Tal Shaked, Erin Renshaw...
WWW
2007
ACM
16 years 6 months ago
Supervised rank aggregation
This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by...
Yu-Ting Liu, Tie-Yan Liu, Tao Qin, Zhiming Ma, Han...
WWW
2006
ACM
16 years 6 months ago
Topical TrustRank: using topicality to combat web spam
Web spam is behavior that attempts to deceive search engine ranking algorithms. TrustRank is a recent algorithm that can combat web spam. However, TrustRank is vulnerable in the s...
Baoning Wu, Vinay Goel, Brian D. Davison
WISE
2005
Springer
15 years 11 months ago
Document Re-ranking by Generality in Bio-medical Information Retrieval
Document ranking is well known to be a crucial process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. ...
Xin Yan, Xue Li, Dawei Song
ICTIR
2009
Springer
16 years 15 days ago
Modeling Expected Utility of Multi-session Information Distillation
Abstract. An open challenge in information distillation is the evaluation and optimization of the utility of ranked lists with respect to flexible user interactions over multiple ...
Yiming Yang, Abhimanyu Lad