I am interested in building a smart music recommendation system using ListenBrainz data. The main goal of this project is to analyze user listening history and provide personalized music recommendations in a simple and efficient way. I chose this idea because recommendation systems are a core part of modern music platforms, and I want to explore how similar functionality can be implemented using open data from MetaBrainz.
The system will work by collecting user listening data such as tracks, artists, and frequency of listens. Initially, I plan to implement a basic recommendation approach using similarity techniques like cosine similarity or collaborative filtering. For example, users with similar listening patterns can be grouped together, and tracks liked by similar users can be recommended. As the project progresses, I may explore simple clustering methods to improve recommendation quality.
The architecture will include a backend system (likely in Python) that processes ListenBrainz data, computes similarities, and generates recommendations. A simple API layer can be added to fetch recommendations for a user. Optionally, a minimal UI or command-line interface can be provided to demonstrate the results.
This project can benefit MetaBrainz by enhancing user engagement in ListenBrainz, as users will be able to discover new music based on their listening habits. It also aligns with the goal of making open music data more useful and interactive. Additionally, the system can be designed in a modular way so it can be extended or integrated into existing MetaBrainz tools in the future.
I would appreciate feedback from the community regarding the feasibility of this idea, suggestions for improvement, and guidance on how best to integrate it with existing systems. I am also willing to start contributing to the codebase to better understand the project before finalizing my proposal.
+----------------------+
| ListenBrainz API |
+----------+-----------+
|
v
+----------------------+
| Data Collection |
| (User listening logs)|
+----------+-----------+
|
v
+----------------------+
| Data Processing |
| (Cleaning & Filtering)|
+----------+-----------+
|
v
+----------------------+
| Recommendation Engine|
| (Similarity / ML) |
+----------+-----------+
|
v
+----------------------+
| API Layer (Python) |
+----------+-----------+
|
v
+----------------------+
| UI / CLI Interface |
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