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

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Advances in Information Retrieval (ECIR 2024)

Abstract

When devising recommendation services, it is important to account for the interests of all content providers, encompassing not only newcomers but also minority demographic groups. In various instances, certain provider groups find themselves underrepresented in the item catalog, a situation that can influence recommendation results. Hence, platform owners often seek to regulate the exposure of these provider groups in the recommended lists. In this paper, we propose a novel cost-sensitive approach designed to guarantee these target exposure levels in pairwise recommendation models. This approach quantifies, and consequently mitigate, the discrepancies between the volume of recommendations allocated to groups and their contribution in the item catalog, under the principle of equity. Our results show that this approach, while aligning groups exposure with their assigned levels, does not compromise to the original recommendation utility. Source code and pre-processed data can be retrieved at https://github.com/alessandraperniciano/meta-learning-strategy-fair-provider-exposure.

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Notes

  1. 1.

    While gender is by no means a binary construct, to the best of our knowledge, no data set with non-binary gender exists. What we are considering is a binary feature.

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Acknowledgement

We acknowledge financial support under the National Recovery and Resilience Plan (NRRP), Miss. 4 Comp. 2 Inv. 1.5 - Call for tender No.3277 published on Dec 30, 2021 by the Italian Ministry of University and Research (MUR) funded by the European Union - NextGenerationEU. Prj. Code ECS0000038 eINS Ecosystem of Innovation for Next Generation Sardinia, CUP F53C22000430001, Grant Assignment Decree N. 1056, Jun 23, 2022 by the MUR.

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Correspondence to Mirko Marras .

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Boratto, L., Cerniglia, G., Marras, M., Perniciano, A., Pes, B. (2024). A Cost-Sensitive Meta-learning Strategy for Fair Provider Exposure in Recommendation. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14610. Springer, Cham. https://doi.org/10.1007/978-3-031-56063-7_36

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  • DOI: https://doi.org/10.1007/978-3-031-56063-7_36

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