Music streaming services heavily rely on recommender systems to improve their users' experience, by helping them navigate through a large musical catalog and discover new songs, albums or artists. However, recommending relevant and personalized content to new users, with few to no interactions with the catalog, is challenging. This is commonly referred to as the user cold start problem. In this applied paper, we present the system recently deployed on the music streaming service Deezer to address this problem. The solution leverages a semi-personalized recommendation strategy, based on a deep neural network architecture and on a clustering of users from heterogeneous sources of information. We extensively show the practical impact of this system and its effectiveness at predicting the future musical preferences of cold start users on Deezer, through both offline and online large-scale experiments. Besides, we publicly release our code as well as anonymized usage data from our experiments. We hope that this release of industrial resources will benefit future research on user cold start recommendation.

翻译：音乐流传服务在很大程度上依赖推荐者系统来改善用户的经验,帮助他们通过大型音乐目录浏览并发现新的歌曲、专辑或艺术家。 然而,向新用户推荐相关和个性化的内容,而与目录没有多少或很少互动,这具有挑战性。 这通常被称为用户冷却启动问题。 在本应用文件中,我们介绍了最近安装在音乐流传服务Deezer上的系统,以解决这一问题。 解决方案利用半个性化建议战略,其基础是深厚的神经网络架构和来自不同信息来源的用户群。 我们广泛展示了这个系统的实际影响及其通过离线和在线大规模实验预测未来迪泽冷启动用户的音乐偏好的有效性。 此外,我们公开发布我们的代码以及我们实验中的匿名使用数据。 我们希望,这种工业资源的释放将有利于未来对用户冷启动建议的研究。