论文标题
时刻音乐推荐系统：在协作过滤方法中建模隐式用户偏好和用户侦听习惯的演变
Time-Aware Music Recommender Systems: Modeling the Evolution of Implicit User Preferences and User Listening Habits in A Collaborative Filtering Approach
论文作者
论文摘要
在线流媒体服务已成为听音乐的最受欢迎方式。这些服务中的大多数都具有推荐机制，可帮助用户发现可能从可用的音乐中吸引他们的歌曲和艺术家。但是，许多人可能无法考虑上下文方面或不断发展的用户行为。因此，有必要开发考虑这些方面的系统。在音乐领域，时间是影响用户偏好并管理其效果的最重要因素之一，也是本文介绍的工作背后的动机。在这里，检查了有关播放歌曲的时间信息。目的是以不断发展的隐式评级和用户的听力行为的形式对用户偏好的演变进行建模。在这项工作中提出的协作过滤方法中，捕获了每天的听力习惯，以表征用户并为他们提供更可靠的建议。验证的结果证明，这种方法在生成上下文感知和无上下文建议方面优于其他方法
Online streaming services have become the most popular way of listening to music. The majority of these services are endowed with recommendation mechanisms that help users to discover songs and artists that may interest them from the vast amount of music available. However, many are not reliable as they may not take into account contextual aspects or the ever-evolving user behavior. Therefore, it is necessary to develop systems that consider these aspects. In the field of music, time is one of the most important factors influencing user preferences and managing its effects, and is the motivation behind the work presented in this paper. Here, the temporal information regarding when songs are played is examined. The purpose is to model both the evolution of user preferences in the form of evolving implicit ratings and user listening behavior. In the collaborative filtering method proposed in this work, daily listening habits are captured in order to characterize users and provide them with more reliable recommendations. The results of the validation prove that this approach outperforms other methods in generating both context-aware and context-free recommendations
