Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.

中文翻译：

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基于乡村原型的听众建模和上下文感知音乐推荐

听者的文化和社会经济背景在很大程度上影响了音乐偏好，这在很大程度上反映在特定国家/地区的音乐收听概况中。以前的工作已经确定了所收听的音乐艺术家的流行分布的几个特定国家的差异。特别是，“音乐主流”的构成因国家而异。为了补充和扩展这些结果，手头的文章提供了以下主要贡献：首先，使用最先进的无监督学习技术，我们确定并彻底调查 (1) 在音乐曲目的细粒度级别上的音乐偏好的国家概况（与早期依赖于艺术家级别的音乐偏好的工作相反）和 (2) 包含具有相似模式的国家的国家原型的听力偏好。其次，我们制定了四个用户模型，利用用户的音乐偏好国家信息。其中，我们提出了一种用户建模方法，将音乐听众描述为已识别国家集群或原型的相似性向量。第三，我们提出了一种利用隐式用户反馈的上下文感知音乐推荐系统，其中上下文是通过四个用户模型定义的。更准确地说，它是一个基于变分自编码器的多层生成模型，其中上下文特征可以通过门控机制影响推荐。第四，我们在世界各地超过 10 亿条用户收听记录（其中我们在实验中使用了 3.69 亿条记录）的真实世界语料库上彻底评估了所提出的推荐系统和用户模型，并展示了其优点。 vis 不利用这种类型的上下文信息的最先进的算法。