Movie Recommendation Web App with Content-Based Filtering
Pick a movie you like and get ten similar recommendations using TF-IDF and cosine similarity, with posters and details.
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Problem Statement & Abstract
Recommendation systems decide much of what we watch and buy. This project builds one from first principles, so you can explain every step in your viva.
Using the public TMDB 5000 movies dataset, the system combines each film’s genres, keywords, cast, director and overview into a single text profile. TF-IDF converts these profiles into vectors and cosine similarity finds the films closest to the one a user selects. Results are refined by blending similarity with rating and popularity so obscure low-rated films do not dominate.
A React interface lets users search a movie and see ten recommendations with posters, ratings, release year and a short explanation such as "same director" or "similar genre". Users can like movies to get a personalised list built from their likes. A FastAPI service serves recommendations in milliseconds from a precomputed similarity matrix, and a notebook shows the data cleaning and evaluation.
Key Features
- Content-based recommendations from genres, cast, crew and keywords
- TF-IDF vectorisation and cosine similarity
- Re-ranking with rating and popularity
- Reason shown for each recommendation
- Personalised list from liked movies
- Search with autocomplete and posters
- Millisecond responses from precomputed neighbours
- Notebook with data cleaning and evaluation
Suitable For
Everything included
+ GST • about $42 USD
Added to your cart
- Delivered in 10 to 12 working days
- Free revisions for 30 days while your guide reviews
- Report written to your university’s format