Recommendation systems have become essential in modern music streaming platforms, due to the vast amount of content available. A common approach in recommendation systems is collaborative filtering, which suggests content to users based on the preferences of others with similar patterns. However, this method performs poorly in domains where interactions are sparse, such as music. Content-based filtering is an alternative approach that examines the qualities of the items themselves. Prior work has explored a range of content-filtering techniques for music, including genre classification, instrument detection, and lyrics analysis. In the literature review component of this work, we examine these methods in detail. Music emotion recognition is a type of content-based filtering that is less explored but has significant potential. Since a user's emotional state influences their musical choices, incorporating user mood into recommendation systems is an alternative way to personalize the listening experience. In this study, we explore a mood-assisted recommendation system that suggests songs based on the desired mood using the energy-valence spectrum. Single-blind experiments are conducted, in which participants are presented with two recommendations (one generated from a mood-assisted recommendation system and one from a baseline system) and are asked to rate them. Results show that integrating user mood leads to a statistically significant improvement in recommendation quality, highlighting the potential of such approaches.

翻译：推荐系统已成为现代音乐流媒体平台的核心组成部分，这主要源于海量内容的存在。推荐系统中的常用方法是协同过滤，该方法基于具有相似偏好模式的其他用户的选择来向用户推荐内容。然而，在交互稀疏的领域（例如音乐），此方法表现不佳。基于内容的过滤是另一种方法，它考察项目本身的特性。先前的研究已探索了一系列用于音乐的内容过滤技术，包括流派分类、乐器检测和歌词分析。在本工作的文献综述部分，我们将详细检视这些方法。音乐情感识别是一种较少被探索但具有显著潜力的基于内容的过滤方法。由于用户的情绪状态会影响其音乐选择，将用户情绪纳入推荐系统是另一种个性化聆听体验的方式。在本研究中，我们探索了一种情绪辅助推荐系统，该系统利用能量-效价谱，基于期望的情绪来推荐歌曲。我们进行了单盲实验，向参与者呈现两种推荐（一种由情绪辅助推荐系统生成，另一种由基线系统生成），并要求他们进行评分。结果表明，整合用户情绪能带来统计上显著的推荐质量提升，凸显了此类方法的潜力。