Design and evaluate an adaptive music recommendation system for personalized listening experiences

Thesis Abstract

The proliferation of digital music streaming platforms has transformed the landscape of music consumption, necessitating the development of intelligent recommendation systems that can enhance personalized listening experiences. Despite advancements in collaborative filtering and content-based filtering techniques, existing systems often lack adaptability to individual user preferences over time, resulting in suboptimal user engagement and satisfaction. This study aims to design and evaluate an adaptive music recommendation system that dynamically personalizes playlists based on evolving user preferences, contextual factors, and listening behavior. The specific objectives are to (1) analyze user listening patterns and preferences; (2) develop an adaptive recommendation algorithm utilizing machine learning techniques, particularly reinforcement learning and clustering; (3) implement the system within a digital music platform prototype; and (4) evaluate system effectiveness through user experience assessment and engagement metrics. Employing a mixed-methods research design, this study combines quantitative data analysis with qualitative user feedback. The population comprises active users of a popular music streaming service, with a sample size of 300 participants selected through stratified random sampling to ensure representativeness across demographic groups, including age, gender, and listening habits. Quantitative data are collected via system-generated logs capturing listening duration, skip rates, and playlist diversity, complemented by surveys measuring user satisfaction and perceived personalization. Qualitative data are obtained through semi-structured interviews with 30 participants to explore user perceptions of system adaptability and relevance. Data analysis involves descriptive statistics, time-series analysis to identify evolving preferences, and inferential techniques such as multiple regression and ANOVA to assess the impact of the adaptive system on user engagement and satisfaction. Thematic analysis is applied to interview transcripts to extract user perceptions and experiential insights. Expected findings indicate that the adaptive music recommendation system significantly enhances personalization, evidenced by increased session durations, reduced skip rates, and higher satisfaction scores compared to baseline non-adaptive systems. The system's ability to learn user preferences over time leads to more relevant recommendations, fostering deeper engagement and potential retention. The study also anticipates identifying key factors influencing user perceptions of system adaptability, including perceived relevance, novelty, and system transparency. The findings will contribute to the theoretical understanding of adaptive recommendation mechanisms, integrating the User-Based Collaborative Filtering theory with Reinforcement Learning frameworks, and extending existing models of personalized content delivery. This research advances knowledge by demonstrating the feasibility and efficacy of integrating machine learning algorithms into real-world music recommendation platforms to facilitate dynamic personalization. The study's contributions include the development of a functional prototype and empirical validation of adaptive techniques, addressing gaps in current literature regarding long-term adaptability and user-driven personalization in music recommendation systems. The main conclusion underscores that adaptive systems significantly improve listener satisfaction and engagement, provided that system transparency and user control are prioritized. Based on these findings, the study recommends that digital platforms incorporate adaptive recommendation algorithms to enhance user experience continually and tailor content dynamically. Future research should explore scalability across diverse user populations, integration with multimodal data such as contextual information (e.g., mood, activity), and the ethical implications of adaptive personalization, including privacy considerations. Overall, this research underscores the potential of machine learning-driven adaptive systems to redefine personalized digital music consumption and user engagement strategies.