Timely Personalization at Peloton: A System and Algorithm for Boosting Time-Relevant Content

Shayak Banerjee, Vijay Pappu, Nilothpal Talukder, Shoya Yoshida, Arnab Bhadury, Allison Schloss, Jasmine Paulino

Abstract: At Peloton, we are challenged to not just surface relevant recommendations of fitness classes to our members, but also timely ones. As our fitness content library expands, we continually produce classes on certain themes which are most timely during a narrow time window. To address this challenge, we provide some control over our recommendations to external stakeholders, such as production and marketing teams. They enter timed boosts of certain classes during the windows they are relevant in. We have built out algorithms which take these desired classes and elevate the number of impressions for them, while preserving members’ engagement with our recommendations. In this paper, we discuss the system, the algorithms and some results from a few A/B tests showing how boosting works in practice.