Proceedings of the 36th International Conference on Machine Learning, PMLR 97:3856-3865, 2019.
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.
@InProceedings{pmlr-v97-li19f,
title = {Online Learning to Rank with Features},
author = {Li, Shuai and Lattimore, Tor and Szepesvari, Csaba},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {3856--3865},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
address = {Long Beach, California, USA},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/li19f/li19f.pdf},
url = {http://proceedings.mlr.press/v97/li19f.html},
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.}
}
%0 Conference Paper
%T Online Learning to Rank with Features
%A Shuai Li
%A Tor Lattimore
%A Csaba Szepesvari
%B Proceedings of the 36th International Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2019
%E Kamalika Chaudhuri
%E Ruslan Salakhutdinov
%F pmlr-v97-li19f
%I PMLR
%J Proceedings of Machine Learning Research
%P 3856--3865
%U http://proceedings.mlr.press
%V 97
%W PMLR
%X 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.
Li, S., Lattimore, T. & Szepesvari, C.. (2019). Online Learning to Rank with Features. Proceedings of the 36th International Conference on Machine Learning, in PMLR 97:3856-3865
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