Building a Movie Recommendation Engine
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July 20, 20266 min read

Building a Movie Recommendation Engine

How I built a content-based movie recommendation system that suggests 5 similar movies instantly using NLP techniques.

What I Built

A content-based recommendation system that takes a movie you love and suggests 5 similar movies. The UI is built with Streamlit for quick prototyping.

How It Works

  1. Data Collection — Used TMDB dataset with 5000+ movies
  2. Feature Extraction — Combined genres, keywords, cast, crew, and overview into a single text blob
  3. Vectorization — Applied CountVectorizer to convert text to numerical vectors
  4. Similarity — Calculated cosine similarity between all movie vectors
  5. Recommendation — For any input movie, return top 5 most similar movies

The Magic of Cosine Similarity

Cosine similarity measures the angle between two vectors. Movies with similar descriptions will have vectors pointing in similar directions, giving a high similarity score.

What I Learned

  • NLP preprocessing (stemming, stop words) dramatically improves results
  • Streamlit is perfect for rapid ML app prototyping
  • Feature engineering matters more than model complexity
Sometimes the simplest approach gives the best results.
Golam Monayem

Golam Monayem

Machine Learning Engineer & Visual Designer