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  • EdwardsLeslieJORS_accepted

    Rights statement: This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 20 Feb 2019 available online:  https://www.tandfonline.com/doi/full/10.1080/01605682.2018.1546650

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Selecting Multiple Web Adverts - a Contextual Multi-armed Bandit with State Uncertainty

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>2/01/2020
<mark>Journal</mark>Journal of the Operational Research Society
Issue number1
Volume71
Number of pages17
Pages (from-to)100-116
Publication statusPublished
Early online date20/02/19
Original languageEnglish

Abstract

We present a method to solve the problem of choosing a set of adverts to display to each of a sequence of web users. The objective is to maximise user clicks over time and to do so we must learn about the quality of each advert in an online manner by observing user clicks. We formulate the problem as a novel variant of a contextual combinatorial multi-armed bandit problem. The context takes the form of a probability distribution over the user's latent topic preference, and rewards are a particular nonlinear function of the selected set and the context. These features ensure that optimal sets of adverts are appropriately diverse. We give a flexible solution method which combines submodular optimisation with existing bandit index policies. User state uncertainty creates ambiguity in interpreting user feedback which prohibits exact Bayesian updating, but we give an approximate method that is shown to work well.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 20 Feb 2019 available online:  https://www.tandfonline.com/doi/full/10.1080/01605682.2018.1546650