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

Autor:	Nazari, Zahra, Charbuillet, Christophe, Pages, Johan, Laurent, Martin, Charrier, Denis, Vecchione, Briana, Carterette, Ben
Rok vydání:	2020
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1145/3397271.3401101
Popis:	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 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.13287 Zobrazit plný text záznamu View this record from Arxiv