Emotion-Aware Conversational Music Recommendation With Multiagent System

Most existing music recommendation systems struggle to perceive users’ implicit emotional states and fail to adapt dynamically to evolving preferences in emotionally rich, context-sensitive scenarios. To address this limitation, we propose an emotion-aware conversational music recommender built on a multiagent system. The system incorporates specialized agents for emotion recognition, semantic intent analysis, and contextual understanding. It distinguishes between explicit emotions, which are directly expressed by the user (e.g., “I feel anxious”), and implicit emotions inferred from contextual cues such as time, environment, or behaviors the user may not be fully aware of. A dual-memory mechanism models long-term musical preferences using a linear decay function and captures short-term, emotion-driven preferences using exponential decay. To enrich music content understanding, multisource information fusion combines streaming platform suggestions with rich metadata from external repositories. The system employs a large language model (LLM) to conduct multiturn dialogues and generate personalized, explainable recommendations. Experimental results show that the proposed approach significantly outperforms existing platforms (e.g., Spotify and Last.fm) in recommendation accuracy, ranking performance, and Hit Ratio@K. These findings underscore the effectiveness of integrating multiagent collaboration, emotion modeling, memory-augmented user profiling, and multisource data fusion for adaptive, user-centric music recommendation.