ICML 2019oral39 citations
Online Learning to Rank with Features
Shuai Li, Tor Lattimore, Csaba Szepesvari
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.
BibTeX
@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},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/li19f/li19f.pdf},
url = {https://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.}
}