AI/ML & Data
Movie Recommender
Personalized recommendations from collaborative signals.
A recommendation engine that learns taste from rating patterns and suggests titles each user is likely to enjoy — the same mechanics that drive product and content discovery.
01
The problem
Catalogues outgrow browsing fast; without personalization, users see the popular few and miss their own long tail.
02
Our approach
Collaborative filtering over user-item ratings with similarity-based retrieval, evaluated on held-out interactions.
03
The outcome
A working recommender demonstrating the personalization stack we apply to commerce and content catalogues.
Highlights
- Collaborative filtering
- Held-out evaluation
