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@article{169006,
        author = {Preet Kumar and Ojus and Ayush Vashisht},
        title = {Harmonizing Emotions and Music: A Deep Dive into Emotion-Driven Playlist Personalization Using Facial Expressions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {1644-1651},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169006},
        abstract = {In the evolving landscape of music recommendation systems, the ability to tailor playlists based on user emotions presents a novel approach to enhancing user experience. This review paper explores the intersection of emotion recognition and music personalization, focusing on the utilization of facial expression analysis as a pivotal tool for discerning emotional states. We examine various methodologies employed in emotion detection, including machine learning techniques and computer vision algorithms, and their effectiveness in interpreting facial cues. Additionally, the paper reviews existing frameworks for integrating emotional data into music recommendation systems, highlighting the challenges and opportunities presented by this innovative approach. Through a comprehensive analysis of current research and practical applications, we discuss the implications of emotion-driven playlists on user engagement, satisfaction, and the overall impact on the music industry. Ultimately, this review aims to provide insights into the future of personalized music experiences, advocating for the adoption of emotion-aware technologies in enhancing the dynamic relationship between users and music.},
        keywords = {Emotion Recognition, Facial Expression Analysis, Music Recommendation Systems, Playlist Personalization, Machine Learning, Computer Vision, User Experience, Emotional Data Integration, User Engagement, Affective Computing},
        month = {November},
        }