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» Online learning to diversify from implicit feedback
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WWW
2009
ACM
14 years 7 months ago
Matchbox: large scale online bayesian recommendations
We present a probabilistic model for generating personalised recommendations of items to users of a web service. The Matchbox system makes use of content information in the form o...
David H. Stern, Ralf Herbrich, Thore Graepel
SIGCSE
2009
ACM
155views Education» more  SIGCSE 2009»
14 years 7 months ago
Increasing engagement in automata theory with JFLAP
We describe the results from a two-year study with fourteen universities on presenting formal languages in a more visual, interactive and applied manner using JFLAP. In our result...
Susan H. Rodger, Eric Wiebe, Kyung Min Lee, Chris ...
CIKM
2008
Springer
13 years 8 months ago
Are click-through data adequate for learning web search rankings?
Learning-to-rank algorithms, which can automatically adapt ranking functions in web search, require a large volume of training data. A traditional way of generating training examp...
Zhicheng Dou, Ruihua Song, Xiaojie Yuan, Ji-Rong W...
IPSN
2010
Springer
14 years 1 months ago
Online distributed sensor selection
A key problem in sensor networks is to decide which sensors to query when, in order to obtain the most useful information (e.g., for performing accurate prediction), subject to co...
Daniel Golovin, Matthew Faulkner, Andreas Krause
ICML
2006
IEEE
14 years 7 months ago
Iterative RELIEF for feature weighting
RELIEF is considered one of the most successful algorithms for assessing the quality of features. In this paper, we propose a set of new feature weighting algorithms that perform s...
Yijun Sun, Jian Li