Sounder

Github repository of this project

Project Overview

This project focused on developing an AI-driven music recommendation system that leverages sophisticated API integrations with Spotify and OpenAI to deliver personalized music suggestions. The goal was to create a responsive, user-friendly platform that enhances the music discovery experience by accurately aligning recommendations with user preferences.

The homepage of the Sounder music recommendation system sets the stage for a personalized music discovery journey. Featuring a clean, intuitive design, users are greeted with a vibrant and inviting interface where they can begin their search for new musical experiences. The “Find Similar Songs” feature prominently displayed on this page encourages users to explore tracks that match their current vibe, providing a straightforward and engaging user experience.

The Similar Song Finder is a core feature of Sounder, designed to allow users to search for songs by name along with other characteristics such as genre and mood. This functionality not only enhances the user’s ability to discover music that resonates with their personal taste but also introduces them to new and lesser-known tracks. By inputting a song, users can explore a tailored list of suggestions that share similar musical elements, ensuring each discovery is both relevant and exciting.

In the My Account section, users can view their most listened tracks, providing a snapshot of their current musical preferences. This personalized area of the site goes further by offering users the ability to create custom playlists based on their recent listening history. This feature not only enhances user engagement by leveraging their unique listening patterns but also makes the music experience deeply personal and continually adaptive to their evolving tastes.

Challenges and Solutions:

Faced with the challenge of integrating multiple complex APIs, I employed Postman to effectively manage and troubleshoot API requests. The project also addressed the common issue of algorithmic bias in music recommendations by striving to diversify the suggestions beyond mainstream tracks.

Reflections:

This project significantly bolstered my backend development skills, particularly in handling APIs and understanding the nuances of machine learning in practical applications. Despite some challenges in front-end design, the project provided valuable insights into balancing functionality with aesthetics, motivating further development in this area.