This study addresses the deficiency in conventional music recommendation systems by focusing on the vital role of emotions in shaping users music choices. These systems often disregard the emotional context, relying predominantly on past listening behavior and failing to consider the dynamic and evolving nature of users emotional preferences. This gap leads to several limitations. Users may receive recommendations that do not match their current mood, which diminishes the quality of their music experience. Furthermore, without accounting for emotions, the systems might overlook undiscovered or lesser-known songs that have a profound emotional impact on users. To combat these limitations, this research introduces an AI model that incorporates emotional context into the song recommendation process. By accurately detecting users real-time emotions, the model can generate personalized song recommendations that align with the users emotional state. This approach aims to enhance the user experience by offering music that resonates with their current mood, elicits the desired emotions, and creates a more immersive and meaningful listening experience. By considering emotional context in the song recommendation process, the proposed model offers an opportunity for a more personalized and emotionally resonant musical journey.

翻译：本研究针对传统音乐推荐系统的缺陷，聚焦于情感在塑造用户音乐选择中的关键作用展开探讨。现有系统往往忽视情感语境，过度依赖用户历史听歌行为，未能考虑用户情感偏好的动态演变特性。这一缺陷导致多重局限：用户可能收到与当前情绪不匹配的推荐内容，从而降低音乐体验质量；同时，忽视情感因素可能导致系统忽略那些能对用户产生深刻情感共鸣的冷门或鲜为人知的歌曲。为克服这些局限，本研究提出一种将情感语境融入歌曲推荐流程的人工智能模型。通过准确检测用户实时情绪状态，该模型可生成与用户情感状态相匹配的个性化歌曲推荐。该方法通过推荐契合用户当前心境、引发预期情感共鸣的音乐，致力于营造更具沉浸感和深度的聆听体验，从而提升用户体验。通过在歌曲推荐过程中纳入情感语境，所提模型为实现更具个性化和情感共鸣的音乐之旅提供了新契机。