论文标题
心理启发的音乐推荐系统
Psychologically-Inspired Music Recommendation System
论文作者
论文摘要
在过去的几年中，自动推荐系统一直是音乐领域的主要重点，在音乐领域，Spotify，Amazon和Apple等公司正在竞争为用户生成最个性化的音乐建议的能力。开发人员仍未解决的挑战之一是考虑音乐的心理和情感方面。我们的目标是找到一种将用户的个人特质及其当前情绪状态集成到具有协作和基于内容的过滤的单个音乐推荐系统中的方法。我们寻求将听众的个性和当前情绪状态与音频功能联系起来，以建立情感上的MRS。我们根据Spotify API数据进行定量和定性将结果与传统MRS的输出进行比较，以了解我们的进步是否对音乐建议的质量产生了重大影响。
In the last few years, automated recommendation systems have been a major focus in the music field, where companies such as Spotify, Amazon, and Apple are competing in the ability to generate the most personalized music suggestions for their users. One of the challenges developers still fail to tackle is taking into account the psychological and emotional aspects of the music. Our goal is to find a way to integrate users' personal traits and their current emotional state into a single music recommendation system with both collaborative and content-based filtering. We seek to relate the personality and the current emotional state of the listener to the audio features in order to build an emotion-aware MRS. We compare the results both quantitatively and qualitatively to the output of the traditional MRS based on the Spotify API data to understand if our advancements make a significant impact on the quality of music recommendations.
