Poster Abstract: Wear2Rec: An IoT-Driven Context-Aware Music Recommendation

With the proliferation of wearable devices and ubiquitous computing, context-aware music recommendation systems are evolving to deliver more personalized experiences. Traditional methods relying on explicit feedback, such as listening history and song ratings, struggle to adapt to users' dynamic contextual states, limiting their effectiveness. In this paper, we introduce Wear2Rec, a privacy-preserving, IoT-driven music recommendation system that leverages passive physiological, psychological, and environmental data from wearable devices to enhance personalization. At its core, Wear2Rec employs an innovative dual-expert-dual-task network architecture that separately extracts context and music features, minimizing cross-modal interference. Unlike conventional models, it simultaneously optimizes both music recommendation and mood improvement prediction, ensuring both relevant music suggestions and an emotionally supportive listening experience. Experimental results show its superiority, achieving 0.8411 AUC for music recommendation and 0.5928 MAE for mood prediction, outperforming traditional models by integrating emotional adaptation. Wear2Rec represents a significant step forward in human-centric, real-time recommendation systems, setting new standards for personalized music experiences in IoT-driven ubiquitous computing environments.