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

Abstract

Recommender systems are increasingly used to predict and serve content that aligns with user taste, yet the task of matching new users with relevant content remains a challenge. We consider podcasting to be an emerging medium with rapid growth in adoption, and discuss challenges that arise when applying traditional recommendation approaches to address the cold-start problem. Using music consumption behavior, we examine two main techniques in inferring Spotify users preferences over more than 200k podcasts. Our results show significant improvements in consumption of up to 50% for both offline and online experiments. We provide extensive analysis on model performance and examine the degree to which music data as an input source introduces bias in recommendations.

Related

July 2021 | ACL

Modeling Language Usage and Listener Engagement in Podcasts

Sravana Reddy, Mariya Lazarova, Yongze Yu, Rosie Jones

July 2021 | SIGIR

Podcast Metadata and Content: Episode Relevance and Attractiveness in Ad Hoc Search

Ben Carterette, Rosie Jones, Gareth Jones, Maria Eskevich, Sravana Reddy, Ann Clifton, Yongze Yu, Jussi Karlgren and Ian Soboroff

July 2021 | SIGIR

Current Challenges and Future Directions in Podcast Information Access

Rosie Jones, Hamed Zamani, Markus Schedl, Ching-Wei Chen, Sravana Reddy, Ann Clifton, Jussi Karlgren, Helia Hashemi, Aasish Pappu, Zahra Nazari, LongQi Yang, Oguz Semerci, Hugues Bouchard, Ben Carterette