2 research outputs found
Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste
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.Comment: SIGIR 202