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Feature-combination hybrid recommender systems for automated music playlist continuation

机译：用于自动音乐播放列表继续的功能组合混合额推荐系统

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摘要

Music recommender systems have become a key technology to support the interaction of users with the increasingly larger music catalogs of on-line music streaming services, on-line music shops, and personal devices. An important task in music recommender systems is the automated continuation of music playlists, that enables the recommendation of music streams adapting to given (possibly short) listening sessions. Previous works have shown that applying collaborative filtering to collections of curated music playlists reveals underlying playlist-song co-occurrence patterns that are useful to predict playlist continuations. However, most music collections exhibit a pronounced long-tailed distribution. The majority of songs occur only in few playlists and, as a consequence, they are poorly represented by collaborative filtering. We introduce two feature-combination hybrid recommender systems that extend collaborative filtering by integrating the collaborative information encoded in curated music playlists with any type of song feature vector representation. We conduct off-line experiments to assess the performance of the proposed systems to recover withheld playlist continuations, and we compare them to competitive pure and hybrid collaborative filtering baselines. The results of the experiments indicate that the introduced feature-combination hybrid recommender systems can more accurately predict fitting playlist continuations as a result of their improved representation of songs occurring in few playlists.
机译：音乐推荐系统已成为支持用户与在线音乐流媒体服务，在线音乐商店和个人设备越来越大的音乐目录的互动的关键技术。音乐推荐系统中的一个重要任务是音乐播放列表的自动延续，这使得音乐流的推荐适应给予（可能是短期）的收听会话。以前的作品表明，将协作过滤应用于策划音乐播放列表的集合，揭示了底层播放列表 - 歌曲的共同发生模式，这些模式对于预测播放列表持续性是有用的。然而，大多数音乐系列都表现出明显的长尾分布。大多数歌曲仅在很少的播放列表中发生，因此，它们是通过协作过滤所代表的不好。我们介绍了两个功能组合混合推荐系统，通过将策划音乐播放列表中的协作信息与任何类型的歌曲特征向量表示集成来延长协作过滤。我们进行离线实验，以评估所提出的系统恢复播放列表的持续性能，我们将它们与竞争性纯粹和混合协同过滤基线进行比较。实验结果表明，由于他们改善了在很少的播放列表中发生的歌曲的提高，所引入的特征组合混合推荐系统可以更准确地预测拟合播放列表延续。

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