Goethals, Bart

Jeunen, Olivier

Abstract: The contextual bandit paradigm provides a general framework for decision-making under uncertainty. It is theoretically well-defined and well-studied, and many personalisation use-cases can be cast as a bandit learning problem. Because this allows for the direct optimisation of utility metrics that rely on online interventions (such as click-through-rate (CTR)), this framework has become an attractive choice to practitioners. Historically, the literature on this topic has focused on a one-sided, user-focused notion of utility, overall disregarding the perspective of content providers in online marketplaces (for example, musical artists on streaming services). If not properly taken into account \u2013 recommendation systems in such environments are known to lead to unfair distributions of attention and exposure, which can directly affect the income of the providers. Recent work has shed a light on this, and there is now a growing consensus that some notion of \u201cequity of exposure\u201d might be preferable to implement in many recommendation use-cases. We study how the top-K contextual bandit problem relates to issues of disparate exposure, and how this disparity can be minimised. The predominant approach in practice is to greedily rank the top-K items according to their estimated utility, as this is optimal according to the well-known Probability Ranking Principle. Instead, we introduce a configurable tolerance parameter that defines an acceptable decrease in utility for a maximal increase in fairness of exposure. We propose a personalised exposure-aware arm selection algorithm that handles this relevance-fairness trade-off on a user-level, as recent work suggests that users\u2019 openness to randomisation may vary greatly over the global populace. Our model-agnostic algorithm deals with arm selection instead of utility modelling, and can therefore be implemented on top of any existing bandit system with minimal changes. We conclude with a case study on carousel personalisation in music recommendation: empirical observations highlight the effectiveness of our proposed method and show that exposure disparity can be significantly reduced with a negligible impact on user utility

Top-K contextual bandits with equity of exposure

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