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» Rank minimization via online learning
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KDD
2012
ACM
187views Data Mining» more  KDD 2012»
12 years 1 months ago
Online learning to diversify from implicit feedback
In order to minimize redundancy and optimize coverage of multiple user interests, search engines and recommender systems aim to diversify their set of results. To date, these dive...
Karthik Raman, Pannaga Shivaswamy, Thorsten Joachi...
CVPR
2012
IEEE
12 years 1 months ago
Online robust image alignment via iterative convex optimization
In this paper we study the problem of online aligning a newly arrived image to previously well-aligned images. Inspired by recent advances in batch image alignment using low rank ...
Yi Wu, Bin Shen, Haibin Ling
WSDM
2012
ACM
267views Data Mining» more  WSDM 2012»
12 years 6 months ago
Learning to rank with multi-aspect relevance for vertical search
Many vertical search tasks such as local search focus on specific domains. The meaning of relevance in these verticals is domain-specific and usually consists of multiple well-d...
Changsung Kang, Xuanhui Wang, Yi Chang, Belle L. T...
ICML
2010
IEEE
13 years 11 months ago
On the Consistency of Ranking Algorithms
We present a theoretical analysis of supervised ranking, providing necessary and sufficient conditions for the asymptotic consistency of algorithms based on minimizing a surrogate...
John Duchi, Lester W. Mackey, Michael I. Jordan
SIGIR
2011
ACM
13 years 1 months ago
Learning search tasks in queries and web pages via graph regularization
As the Internet grows explosively, search engines play a more and more important role for users in effectively accessing online information. Recently, it has been recognized that ...
Ming Ji, Jun Yan, Siyu Gu, Jiawei Han, Xiaofei He,...