Music search, retrieval, and recommendation systems have experienced a boom dur‐ ing the past few years due to streaming services providing access to huge catalogs anywhere and anytime. These streaming services collect the user behavior in terms of actions on music items, such as play, skip, playlist creation, and modification. As a result, an abundance of user and usage data has been collected and is avail‐ able to companies and academics, allowing for user profiling and to create personal‐ ized music search and recommendation systems. The importance and timeliness of research on such personalized music systems is evidenced by publications in ven‐ ues including the ACM Conference on Recommender Systems, ACM Conference on User Modeling, Adaptation and Personalization, International Society for Music Information Retrieval Conference, the ACM Special Interest Group on Informa‐ tion Retrieval Conference, ACM CHI Conference on Human Factors in Computing Systems, and the ACM International Conference on Intelligent User Interfaces, as well as in journals including IEEE Transactions on Affective Computing and ACM Transactions on Intelligent Systems and Technology, in addition to UMUAI. On the other hand, there are still plenty of unsolved challenges. In particular, scholars have identified as some of the most vital ones: understanding and modeling users, person‐ alization of recommendation and retrieval systems, user adaptivity in interfaces, and context awareness.

中文翻译：

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与音乐个性化交互的用户建模特刊前言

由于流媒体服务可以随时随地访问大量目录，音乐搜索、检索和推荐系统在过去几年中经历了蓬勃发展。这些流媒体服务根据对音乐项目的操作收集用户行为，例如播放、跳过、创建播放列表和修改。因此，大量用户和使用数据已被收集并可供公司和学术机构使用，从而允许进行用户分析并创建个性化的音乐搜索和推荐系统。此类个性化音乐系统研究的重要性和及时性可以通过以下场所的出版物得到证明，包括 ACM 推荐系统会议、ACM 用户建模、适应和个性化会议、国际音乐信息检索协会会议、ACM 信息检索特别兴趣组会议、ACM CHI 计算系统中的人为因素会议和 ACM 智能用户界面国际会议，以及包括 IEEE Transactions on Affective Computing 和 ACM Transactions on Intelligent Systems 在内的期刊技术，除了 UMUAI。另一方面，仍有许多未解决的挑战。特别是，学者们已经确定了一些最重要的因素：理解和建模用户、推荐和检索系统的个性化、界面中的用户适应性和上下文意识。以及包括 IEEE Transactions on Affective Computing 和 ACM Transactions on Intelligent Systems and Technology 在内的期刊，以及 UMUAI。另一方面，仍有许多未解决的挑战。特别是，学者们已经确定了一些最重要的因素：理解和建模用户、推荐和检索系统的个性化、界面中的用户适应性和上下文意识。以及包括 IEEE Transactions on Affective Computing 和 ACM Transactions on Intelligent Systems and Technology 在内的期刊，以及 UMUAI。另一方面，仍有许多未解决的挑战。特别是，学者们已经确定了一些最重要的因素：理解和建模用户、推荐和检索系统的个性化、界面中的用户适应性和上下文意识。