根据用户文字描述实时推荐匹配情绪的音乐。
Words to Waves: Emotion-Adaptive Music Recommendation System
- 用文本情感嵌入捕捉用户实时情绪状态
- 结合深度记忆与广义泛化提升推荐准确率
- 适合情绪敏感型音乐应用开发
当前推荐系统常忽略情绪背景，仅依赖历史听歌记录或静态情绪标签。本文提出一种基于宽深学习变体的音乐推荐框架，直接以自然语言推断的实时情绪状态为输入，推荐能精准表达对应情绪的歌曲。系统通过微调的Transformer嵌入从用户提供的文本描述中捕捉情绪上下文，预测情绪维度（效价-唤醒度）。深度部分利用这些嵌入泛化未见情绪模式，广度部分通过交叉乘积特征有效记忆用户-情绪与情绪-流派关联。实验表明，个性化音乐推荐能正向影响用户情绪，并显著提升情绪相关性。
原文摘要 · Abstract (English)
Current recommendation systems often tend to overlook emotional context and rely on historical listening patterns or static mood tags. This paper introduces a novel music recommendation framework employing a variant of Wide and Deep Learning architecture that takes in real-time emotional states inferred directly from natural language as inputs and recommends songs that closely portray the mood. The system captures emotional contexts from user-provided textual descriptions by using transformer-based embeddings, which were finetuned to predict the emotional dimensions of valence-arousal. The deep component of the architecture utilizes these embeddings to generalize unseen emotional patterns, while the wide component effectively memorizes user-emotion and emotion-genre associations through cross-product features. Experimental results show that personalized music selections positively influence the user's emotions and lead to a significant improvement in emotional relevance.
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