The Diversity of Music Recommender Systems
The Diversity of Music Recommender Systems
复制标题
音乐推荐系统的多样性
DOI：
10.1145/3490100.3516474
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Knijnenburg, Bart Piet
中科院分区：
文献类型：
--
作者：
Baracskay, Ian;Baracskay III, Donald J;Iqbal, Mehtab;Knijnenburg, Bart Piet
虽然音乐流媒体服务用来提供推荐的算法经常在离线、孤立的环境中进行研究，但在系统本身的完整背景下研究它们推荐的性质的研究很少。这项工作试图比较五个最受欢迎的音乐流媒体服务提供的现实世界推荐的多样性水平，给出相同的低、中、高多样性输入项列表。我们通过检查Google Play商店上五项服务的评论，重点关注用户对他们的推荐系统的感知和他们输出的多样性，将我们的结果与背景联系起来。我们发现YouTube音乐提供了最多样化的推荐，但推荐者对这五项服务的看法是相似的。消费者对他们的音乐服务提供的推荐有多种观点--从不想要任何推荐到为帮助他们找到新音乐的算法鼓掌。
While the algorithms used by music streaming services to provide recommendations have often been studied in offline, isolated settings, little research has been conducted studying the nature of their recommendations within the full context of the system itself. This work seeks to compare the level of diversity of the real-world recommendations provided by five of the most popular music streaming services, given the same lists of low-, medium- and high-diversity input items. We contextualized our results by examining the reviews for each of the five services on the Google Play Store, focusing on users’ perception of their recommender systems and the diversity of their output. We found that YouTube Music offered the most diverse recommendations, but the perception of the recommenders was similar across the five services. Consumers had multiple perspectives on the recommendations provided by their music service—ranging from not wanting any recommendations to applauding the algorithm for helping them find new music.
重新审视音乐推荐和发现
DOI：
--
发表时间：
2011
期刊：
ACM Conference on Recommender Systems
影响因子：
--
作者：
Òscar Celma;Paul Lamere
通讯作者：
Paul Lamere
迷人的算法：推荐系统作为陷阱
影响因子：
0.9
作者：
Nick Seaver
通讯作者：
Nick Seaver