Online Learning to Rank with Features

Shuai Li; Tor Lattimore; Csaba Szepesvari

Online Learning to Rank with Features

Shuai Li, Tor Lattimore, Csaba Szepesvari ;

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.

Related Material

@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.