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When devising recommendation services, it is important to account for theinterests of all content providers, encompassing not only newcomers but alsominority demographic groups. In various instances, certain provider groups findthemselves underrepresented in the item catalog, a situation that can influencerecommendation results. Hence, platform owners often seek to regulate theexposure of these provider groups in the recommended lists. In this paper, wepropose a novel cost-sensitive approach designed to guarantee these targetexposure levels in pairwise recommendation models. This approach quantifies,and consequently mitigate, the discrepancies between the volume ofrecommendations allocated to groups and their contribution in the item catalog,under the principle of equity. Our results show that this approach, whilealigning groups exposure with their assigned levels, does not compromise to theoriginal recommendation utility. Source code and pre-processed data can beretrieved athttps://github.com/alessandraperniciano/meta-learning-strategy-fair-provider-exposure.

A Cost-Sensitive Meta-Learning Strategy for Fair Provider Exposure in Recommendation