Music Recommendation System on Spotify Using Deep Learning

Music Recommendation System on Spotify Using Deep Learning

{BE Students}* Pranali Balkrushna Chopade, Nikita Baban Chormale,

 Pratiksha Pratap Shinde,  Vishakha Suryakant Shitole.

{Faculty}**  Sachin Sukhadev Bere,  Dinesh Bhagwan Hanchate

Department of Computer Engineering, Dattakala Group of Institutions Faculty of Engineering, Swami-Chincholi Bhigwan-413130, University of Pune, Maharashtra, INDIA.

vishakhashitole19@gmail.com

Abstract

In the era of personalized digital experiences, music recommendation systems play a vital role in enhancing user satisfaction by delivering contextually relevant content. This project presents a deep learning-based music recommendation system integrated with Spotify that tailors music suggestions based on user age and current weather conditions. The system leverages computer vision techniques for age detection through facial analysis using Convolutional Neural Networks (CNNs), while weather data is retrieved from an external API based on the user's location. By combining these inputs, the system dynamically curates playlists that match both the user's demographic profile and the environmental context. The model is trained on a diverse dataset of facial images for accurate age classification and integrates seamlessly with Spotify's Web API for real-time music playback. Experimental results demonstrate improved user engagement and satisfaction through personalized music experiences. This approach highlights the potential of deep learning in enhancing recommender systems by incorporating both human and environmental factors.