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» Learning to Rank with Supplementary Data
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CORR
2008
Springer
116views Education» more  CORR 2008»
13 years 5 months ago
Learning to rank with combinatorial Hodge theory
Abstract. We propose a number of techniques for learning a global ranking from data that may be incomplete and imbalanced -- characteristics that are almost universal to modern dat...
Xiaoye Jiang, Lek-Heng Lim, Yuan Yao, Yinyu Ye
SDM
2007
SIAM
169views Data Mining» more  SDM 2007»
13 years 6 months ago
Rank Aggregation for Similar Items
The problem of combining the ranked preferences of many experts is an old and surprisingly deep problem that has gained renewed importance in many machine learning, data mining, a...
D. Sculley
WWW
2006
ACM
14 years 6 months ago
Beyond PageRank: machine learning for static ranking
Since the publication of Brin and Page's paper on PageRank, many in the Web community have depended on PageRank for the static (query-independent) ordering of Web pages. We s...
Matthew Richardson, Amit Prakash, Eric Brill
ECIR
2011
Springer
12 years 9 months ago
Fractional Similarity: Cross-Lingual Feature Selection for Search
Abstract. Training data as well as supplementary data such as usagebased click behavior may abound in one search market (i.e., a particular region, domain, or language) and be much...
Jagadeesh Jagarlamudi, Paul N. Bennett
ICML
2009
IEEE
14 years 6 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