A Hybrid Approach to Music Recommendations for Improving ADHD Productivity

This study explores the development of a web application designed to enhance focus, motivation, and productivity in individuals with ADHD by inte-grating music recommendation algorithms, task management tools, and note-taking features. Music recommendations were generated using a hybrid approach combining Collaborative Filtering and Content-based Filtering to create tailored playlists based on tracks users “liked” during the procedure. The application was evaluated through a systematic framework using the Pomodoro Technique, where twenty ADHD-diagnosed students (aged 18+) completed two 20-minute sessions, each followed by a 5-minute break in between. In the first session, participants were asked to listen to one of three pre-selected playlists (Lo-Fi, Classical, Binaural Beats) and indicated their preferences by liking tracks while performing focused tasks. Using participants’ selections from the first session, the recommendation model generated a personalized "For You" playlist during the break, which they engaged with under identical condi-tions in the second session. A mixed-methods analysis was then used to combine quantitative data from Likert scale ratings and qualitative feedback from open-ended responses and structured questionnaires. The results of this study revealed significant improvements in all key areas, supporting the effectiveness of personalized music recommendations in academic and professional settings. Future work will focus on refining the application and expanding the recommendation system to accommodate a broader range of musical preferences.