A 3-stage content-based recommendation pipeline — metadata extraction, vector representation, and cosine similarity scoring — that generates explainable suggestions with no user interaction data required.
3
Pipeline stages
Content-based
No user data needed
Explainable
Traceable recommendations
Problem statement
Recommendation systems need a meaningful content representation before similarity becomes useful, especially when user interaction data is unavailable. The challenge is in the feature engineering: what you extract and how you represent it determines recommendation quality more than the similarity algorithm.
Architecture breakdown
I built a 3-stage pipeline that cleans and extracts metadata features, transforms them into comparable vector representations per title, then applies cosine similarity to identify nearest neighbors — keeping every step explicit and inspectable.
Tech stack explanation
System diagram
[ Movie Metadata ]
|
v
[ Feature Extraction ]
|
v
[ Vector Representation ]
|
v
[ Similarity Engine ]
|
v
[ Recommended Movies ]Key challenges
A content-based recommender that processes movie metadata through 3 explicit pipeline stages: feature extraction, vector representation, and similarity scoring. The design prioritizes explainability — every suggestion is traceable to specific shared metadata signals rather than opaque collaborative filtering.
What I learned