Problem
Users often spend too much time searching for the next track. The project goal was to reduce this friction with fast, relevant recommendations from historical listening preferences.
BeatBuff Case Study
BeatBuff is an AI-based music recommendation system that suggests personalized songs by analyzing listening patterns and audio feature similarity.
Tech Stack
- Python for data processing and model logic
- Pandas and NumPy for feature preparation
- Similarity-based recommendation pipeline
- Matplotlib for exploratory diagnostics
Approach
- Collected and cleaned music metadata and audio feature fields
- Engineered a normalized feature vector for each track
- Calculated nearest-neighbor style similarity for top-N recommendations
- Validated recommendation quality by manual relevance checks and iterative tuning
Results
- Improved recommendation consistency after feature normalization
- Reduced irrelevant suggestions through tuning and filtering
- Produced a reusable pipeline for future model upgrades
Challenges and Solutions
- Cold-start behavior for new users was handled with popularity-biased fallback picks
- Feature imbalance was handled by scaling and selective weighting
- Noise in metadata was reduced with cleaning and rule-based filters