情感感知音乐推荐系统：通过实时情感上下文提升用户体验
Emotion-Aware Music Recommendation System: Enhancing User Experience Through Real-Time Emotional Context
AI总结：
针对传统音乐推荐系统忽视情感上下文的缺陷，本研究提出一种融入实时情感检测的人工智能模型，通过生成与用户当前情感状态相匹配的个性化歌曲推荐，显著提升用户的沉浸式收听体验。
摘要：
本研究通过关注情感在塑造用户音乐选择中的关键作用，解决了传统音乐推荐系统的缺陷。这些系统通常忽视情感上下文，主要依赖过去的收听行为，未能考虑用户情感偏好的动态和演变特性。这种差距导致了几个局限性。用户可能会收到与他们当前心情不匹配的推荐，这降低了他们的音乐体验质量。此外，如果不考虑情感，系统可能会忽略那些对用户有深刻情感影响的未被发现或鲜为人知的歌曲。为了克服这些局限性，本研究引入了一个将情感上下文纳入歌曲推荐过程的人工智能模型。通过准确检测用户的实时情感，该模型能够生成与用户情感状态相一致的个性化歌曲推荐。这种方法旨在通过提供与用户当前心情产生共鸣、唤起期望情感的音乐，创造一种更具沉浸感和有意义的收听体验，从而提升用户体验。通过在歌曲推荐过程中考虑情感上下文，所提出的模型为更个性化和情感共鸣的音乐之旅提供了机会。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
