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 appl...
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| Veröffentlicht in: | arXiv.org 2020-07 |
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| creator | Nazari, Zahra Charbuillet, Christophe Pages, Johan Martin, Laurent Charrier, Denis Vecchione, Briana Carterette, Ben |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2007.13287 |
| format | Article |
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| subjects | Cold starts Computer Science - Information Retrieval Computer Science - Learning Consumption Recommender systems Taste |
| title | Recommending Podcasts for Cold-Start Users Based on Music Listening and Taste |
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