395 search results - page 19 / 79 » Learning to rank with partially-labeled data |
118
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SIGIR
2009
ACM
15 years 8 months ago
2009
ACM
Incorporating features extracted from clickthrough data (called clickthrough features) has been demonstrated to significantly improve the performance of ranking models for Web sea...
94
Voted
RECSYS
2009
ACM
15 years 8 months ago
2009
ACM
A fundamental aspect of rating-based recommender systems is the observation process, the process by which users choose the items they rate. Nearly all research on collaborative ...
118
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ICML
2009
IEEE
16 years 2 months ago
2009
IEEE
This paper gives an efficient Bayesian method for inferring the parameters of a PlackettLuce ranking model. Such models are parameterised distributions over rankings of a finite s...
139
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KDD
2012
ACM
13 years 4 months ago
2012
ACM
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...
130
Voted
NIPS
2003
15 years 3 months ago
2003
Label ranking is the task of inferring a total order over a predefined set of labels for each given instance. We present a general framework for batch learning of label ranking f...