Movie Recommender

The problem

Most student recommenders stop at “it returns movies.” I wanted one that could answer a harder question: which collaborative-filtering approach is actually better on this data, and by how much?

Approach

• Built a FastAPI + PostgreSQL backend serving top-K neighbor predictions over a normalized schema (users, movies, ratings, tags, links).
• Implemented both user-based and item-based collaborative filtering with Pearson and cosine similarity over mean-centered ratings.
• Cleaned the data the way it actually needs cleaning: dedupe, keep-latest on repeat ratings, filter low-activity users/items, and a time-aware train/test split so the test set represents future behavior.
• Added a baseline correction (global mean + user bias + item bias) and evaluated with RMSE / MAE plus Precision@K rather than eyeballing results.
• Built the frontend in React 19 + Vite + Tailwind against the REST API.

Impact

• Side-by-side, measured comparison of user- vs item-based CF on a held-out, time-aware split.
• Clean separation: typed REST API, normalized Postgres schema, reproducible preprocessing.

Stack