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ECIR
2016
Springer

Probabilistic Multileave Gradient Descent

4 years 6 months ago
Probabilistic Multileave Gradient Descent
Online learning to rank methods aim to optimize ranking models based on user interactions. The dueling bandit gradient descent (DBGD) algorithm is able to effectively optimize linear ranking models solely from user interactions. We propose an extension of DBGD, called probabilistic multileave gradient descent (PMGD) that builds on probabilistic multileave, a recently proposed highly sensitive and unbiased online evaluation method. We demonstrate that P-MGD significantly outperforms state-of-the-art online learning to rank methods in terms of online performance, without sacrificing offline performance and at greater learning speed.
Harrie Oosterhuis, Anne Schuth, Maarten de Rijke
Added 02 Apr 2016
Updated 02 Apr 2016
Type Journal
Year 2016
Where ECIR
Authors Harrie Oosterhuis, Anne Schuth, Maarten de Rijke
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