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Recommender systems are increasingly used to predict and serve content thataligns with user taste, yet the task of matching new users with relevantcontent remains a challenge. We consider podcasting to be an emerging mediumwith rapid growth in adoption, and discuss challenges that arise when applyingtraditional recommendation approaches to address the cold-start problem. Usingmusic consumption behavior, we examine two main techniques in inferring Spotifyusers preferences over more than 200k podcasts. Our results show significantimprovements in consumption of up to 50\% for both offline and onlineexperiments. We provide extensive analysis on model performance and examine thedegree to which music data as an input source introduces bias inrecommendations.

Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste