Recommendation systems are essential in modern music streaming platforms due to the vast amount of available content. While collaborative filtering is widely used to suggest items based on the preferences of others with similar patterns, it performs poorly in domains where user-item interactions are sparse, such as music. Content-based filtering is an alternative approach that examines the qualities of the items themselves. Genre, instrumentation, and lyrics have been explored; however, relatively little attention has been given to emotion recognition. Since a user's emotional state strongly influences their music choice, incorporating mood signals offers a promising direction for personalization. In this work, we propose a mood-conditioned ranking framework that integrates user affective signals into the recommendation process via softmax-based sampling in the energy-valence space. We evaluate the approach via single-blind experiments in which participants compare recommendations from the proposed system against a baseline. The results indicate improved perceived recommendation quality, providing preliminary evidence for the effectiveness of incorporating mood-based inputs into music recommendations.

翻译：推荐系统在现代音乐流媒体平台中至关重要，这源于海量可用的音乐内容。虽然协同过滤被广泛应用于基于相似模式用户偏好来进行项目推荐，但在用户-项目交互稀疏的领域（如音乐）中，其表现欠佳。基于内容的过滤是另一种方法，它考察项目本身的属性。诸如音乐类型、配器与歌词等因素已被探究，然而，情感识别受到的关注相对较少。由于用户情绪状态强烈影响其音乐选择，融入情绪信号为个性化推荐提供了有前景的方向。在本工作中，我们提出一个情绪条件化的排序框架，通过能量-效价空间中的softmax采样，将用户情感信号融入推荐过程。我们通过单盲实验评估该方法，由参与者将所提系统的推荐结果与基线系统进行对比。结果表明，感知推荐质量得到改善，这为将基于情绪的输入融入音乐推荐的有效性提供了初步证据。