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Modeling position bias ranking for streaming media services

By Matteo Ruffini, Vito Bellini, Alexander Buchholz, Giuseppe Di Benedetto, Yannik Stein
2022
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We tackle the problem of position bias estimation for streaming media services. Position bias is a widely studied topic in ranking literature and its impact on ranking quality is well understood. Although several methods exist to estimate position bias, their applicability to an industrial setting is limited, either because they require ad-hoc interventions that harm user experience, or because their learning accuracy is poor. In this paper, we present a novel position bias estimator that overcomes these limitations: it can be applied to streaming media services without manual interventions while delivering best in class estimation accuracy. We compare the proposed method against existing ones on real and synthetic data and illustrate its applicability to Amazon Music.
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