论文标题
以用户为中心的个人音乐之旅的调查
A User-Centered Investigation of Personal Music Tours
论文作者
论文摘要
流服务使用推荐系统将正确的音乐呈现给用户。播放列表是一种以列表方式呈现音乐的流行方式，即作为一首歌列表。另一种是巡回演出，歌曲替代了曲目，这些曲目解释了连续歌曲之间的联系。旅游满足了用户寻求有关歌曲的背景信息的需求，并且在正确的用户环境下，发现与播放列表相比优越。在这项工作中，我们首次使用半结构化访谈对两种旅游算法（贪婪和最佳）进行以用户为中心的评估。我们评估算法，讨论算法产生的旅行的属性，我们确定哪些属性是可取的，哪些属性是不可能的，我们列举了对算法的一些可能改进，以及如何实施改进的实际建议。我们的主要发现是，贪婪产生的旅行比最佳之旅更为令人愉悦，而三个重要的旅行属性是多样性，歌曲的安排和歌曲熟悉度。更一般而言，我们提供有关如何向用户展示音乐的见解，这可以为以用户为中心的推荐系统设计提供信息。
Streaming services use recommender systems to surface the right music to users. Playlists are a popular way to present music in a list-like fashion, ie as a plain list of songs. An alternative are tours, where the songs alternate segues, which explain the connections between consecutive songs. Tours address the user need of seeking background information about songs, and are found to be superior to playlists, given the right user context. In this work, we provide, for the first time, a user-centered evaluation of two tour-generation algorithms (Greedy and Optimal) using semi-structured interviews. We assess the algorithms, we discuss attributes of the tours that the algorithms produce, we identify which attributes are desirable and which are not, and we enumerate several possible improvements to the algorithms, along with practical suggestions on how to implement the improvements. Our main findings are that Greedy generates more likeable tours than Optimal, and that three important attributes of tours are segue diversity, song arrangement and song familiarity. More generally, we provide insights into how to present music to users, which could inform the design of user-centered recommender systems.
