Online Learning to Rank with Features
Machine Learning
2019-05-28 v2 Machine Learning
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.
Keywords
Cite
@article{arxiv.1810.02567,
title = {Online Learning to Rank with Features},
author = {Shuai Li and Tor Lattimore and Csaba Szepesvári},
journal= {arXiv preprint arXiv:1810.02567},
year = {2019}
}