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ICML
2009
IEEE
16 years 2 months ago
BoltzRank: learning to maximize expected ranking gain
Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to o...
Maksims Volkovs, Richard S. Zemel
SDM
2011
SIAM
414views Data Mining» more  SDM 2011»
14 years 4 months ago
Clustered low rank approximation of graphs in information science applications
In this paper we present a fast and accurate procedure called clustered low rank matrix approximation for massive graphs. The procedure involves a fast clustering of the graph and...
Berkant Savas, Inderjit S. Dhillon
IRAL
2003
ACM
15 years 7 months ago
Temporal ranking for fresh information retrieval
In business, the retrieval of up-to-date, or fresh, information is very important. It is difficult for conventional search engines based on a centralized architecture to retrieve ...
Nobuyoshi Sato, Minoru Uehara, Yoshifumi Sakai
NAACL
2010
14 years 11 months ago
Constraint-Driven Rank-Based Learning for Information Extraction
Most learning algorithms for undirected graphical models require complete inference over at least one instance before parameter updates can be made. SampleRank is a rankbased lear...
Sameer Singh, Limin Yao, Sebastian Riedel, Andrew ...
WAOA
2007
Springer
158views Algorithms» more  WAOA 2007»
15 years 8 months ago
Deterministic Algorithms for Rank Aggregation and Other Ranking and Clustering Problems
We consider ranking and clustering problems related to the aggregation of inconsistent information. Ailon, Charikar, and Newman [1] proposed randomized constant factor approximatio...
Anke van Zuylen, David P. Williamson