Transparently Serving the Public: Enhancing Public Service Media Values through Exploration

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Abstract

In the last few years, we have reportedly underlined the importance of the Public Service Media Remit for ZDF as a Public Service Media provider. Offering fair, diverse, and useful recommendations to users is just as important for us as being transparent about our understanding of these values, the metrics that we are using to evaluate their extent, and the algorithms in our system that produce such recommendations. This year, we have made a major step towards transparency of our algorithms and metrics describing them for a broader audience, offering the possibility for the audience to learn details about our systems and to provide direct feedback to us. Having the possibility to measure and track PSM metrics, we have started to improve our algorithms towards PSM values. In this work, we describe these steps and the results of actively debasing and adding exploration into our recommendations to achieve more fairness.

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APA

Grün, A., & Neufeld, X. (2023). Transparently Serving the Public: Enhancing Public Service Media Values through Exploration. In Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 (pp. 1045–1048). Association for Computing Machinery, Inc. https://doi.org/10.1145/3604915.3610243

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