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arXiv · 1810.02567

Online Learning to Rank with Features

Shuai Li·Tor Lattimore·Csaba Szepesvári
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Abstract

We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.

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Shuai Li, Tor Lattimore, Csaba Szepesvári. 2018-10-05. Online Learning to Rank with Features. https://arxiv.org/abs/1810.02567

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