Music recommendation methods using the MR-CTCFI algorithm

With the rapid growth of digital music services, personalized music recommendation has become an essential part of enhancing user experience. Traditional recommendation algorithms, primarily relying on collaborative filtering, often struggle to effectively capture complex relationships between users and music, especially when considering dynamic factors like time and context. To address these limitations, this paper introduces the MR-CTCFI (Multi-Relation Collaborative Temporal Contextual Feature Interaction) algorithm. The MR-CTCFI algorithm integrates multi-relational data, including user-item interactions and item-item similarities, with temporal and contextual features, enabling the model to better understand evolving user preferences. By leveraging collaborative filtering alongside temporal context and feature interactions, the algorithm improves the precision and adaptability of recommendations over time. Experimental results on diverse music datasets demonstrate that the MR-CTCFI-based method outperforms traditional approaches, offering more accurate, context-aware, and dynamic recommendations. This work contributes to the field of music recommendation systems by providing a novel framework that incorporates complex data interactions and temporal dynamics to enhance user satisfaction and recommendation quality.