International Journal of Scientific Research and Engineering Development

The rapid growth of music streaming platforms has significantly transformed the way audiences discover and consume music. Platforms such as Spotify and YouTube Music rely extensively on artificial intelligence (AI)–based recommendation algorithms to personalize user experiences by suggesting songs, playlists, and artists aligned with individual preferences. These AI-driven recommendation systems play a crucial role in shaping listener behaviour by influencing music discovery, listening patterns, engagement levels, and platform loyalty. The present study aims to examine the influence of AI recommendation algorithms on listener behaviour on Spotify and YouTube Music. The research adopts a descriptive and analytical approach, using primary data collected through a structured questionnaire administered to active music streaming users. Secondary data were collected from academic journals, industry reports, and digital media studies related to AI and music streaming platforms. The findings of the study are expected to provide insights into how AI-based personalization affects user satisfaction, trust in recommendations, and overall engagement with music streaming platforms. The study also offers practical implications for music marketers, independent artists, and streaming platforms in effectively leveraging AI-driven recommendations for audience engagement and content promotion.

Aditya Bharate, Dhruv Raj Pandita, John Marcus, "AI Recommendation Algorithms and their Influence on Listener Behaviour on Music Streaming Platforms: A Study of Spotify and YouTube Music" International Journal of Scientific Research and Engineering Development, V8(6): Page(2828-2833) Nov-Dec 2025. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.