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» A Probability Model for Combining Ranks
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ICML
2009
IEEE
16 years 13 days 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
RECSYS
2010
ACM
14 years 9 months ago
List-wise learning to rank with matrix factorization for collaborative filtering
A ranking approach, ListRank-MF, is proposed for collaborative filtering that combines a list-wise learning-to-rank algorithm with matrix factorization (MF). A ranked list of item...
Yue Shi, Martha Larson, Alan Hanjalic
IPM
2008
93views more  IPM 2008»
14 years 11 months ago
A new robust relevance model in the language model framework
ct 8 In this paper, a new robust relevance model is proposed that can be applied to both pseudo and true relevance feedback 9 in the language-modeling framework for document retrie...
Xiaoyan Li
SIAMJO
2011
14 years 2 months ago
Rank-Sparsity Incoherence for Matrix Decomposition
Suppose we are given a matrix that is formed by adding an unknown sparse matrix to an unknown low-rank matrix. Our goal is to decompose the given matrix into its sparse and low-ran...
Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Pa...
ECML
2007
Springer
15 years 5 months ago
An Unsupervised Learning Algorithm for Rank Aggregation
Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as op...
Alexandre Klementiev, Dan Roth, Kevin Small