Discover Your Next Favorite Movie

Enter a movie you love, and our content-based recommender will analyze thousands of films to find the perfect matches.

Inside the Algorithm

How CineMatch AI computes your recommendations

Metadata Tagging

The algorithm extracts and processes title, genre keywords, release year, and director metadata into a unified tag corpus for each of the 16,252 movies.

Count Vectorization

Tags are tokenized and transformed into a 5,000-dimensional sparse frequency matrix. This filters out noise words while amplifying common structural patterns.

Cosine Similarity

Recommendations are computed on-the-fly by measuring the cosine angle between the selected movie's tag vector and all 16,251 other vectors.

Model Specifications

  • Dataset size: 16,252 curated movies (IMDb top rating & popularity)
  • Feature limit: 5,000 unique stems (English stop words excluded)
  • Backend engine: Python, FastAPI, Pandas, Scikit-learn (sparse implementation)
  • In-Memory overhead: < 8MB (Sparse CSR matrix representation)