论文标题
使用用户听力数据学习音频嵌入，用于基于内容的音乐建议
Learning Audio Embeddings with User Listening Data for Content-based Music Recommendation
论文作者
论文摘要
在新曲目发行上的个性化建议一直是音乐行业中一个具有挑战性的问题。为了解决这个问题，我们首先探索用户的听力历史记录和人口统计信息，以构建代表用户音乐偏好的用户。通过用户喜欢和不喜欢的轨道的用户嵌入和音频数据，可以使用siamese网络使用公制学习来获得每个轨道的音频嵌入。对于新曲目，我们可以通过分别计算曲目的音频嵌入和不同用户嵌入之间的相似性来决定最佳的用户组。所提出的系统在基于内容的音乐推荐中产生最先进的性能，并通过数百万用户和曲目进行了测试。此外，我们提取音频嵌入作为音乐类型分类任务的功能。结果显示了我们音频嵌入的概括能力。
Personalized recommendation on new track releases has always been a challenging problem in the music industry. To combat this problem, we first explore user listening history and demographics to construct a user embedding representing the user's music preference. With the user embedding and audio data from user's liked and disliked tracks, an audio embedding can be obtained for each track using metric learning with Siamese networks. For a new track, we can decide the best group of users to recommend by computing the similarity between the track's audio embedding and different user embeddings, respectively. The proposed system yields state-of-the-art performance on content-based music recommendation tested with millions of users and tracks. Also, we extract audio embeddings as features for music genre classification tasks. The results show the generalization ability of our audio embeddings.
