Movie Recommendation System

Movie Recommendation Image 1
Movie Recommendation Image 2

Project Information

  • Category: Machine-Learning
  • Project date: March 20, 2024
  • Project URL: GitHub Repo

Movie Recommendation System: Content-Based Approach

This project features a content-based movie recommendation system that suggests films to users by analyzing movie metadata and user-selected preferences. It focuses on attributes like genres, keywords, and overviews to identify and recommend movies similar to those a user enjoys.

The system leverages Natural Language Processing (NLP) methods such as tokenization, stemming, and cosine similarity for comparing movie features. Built in Python using libraries including pandas, scikit-learn, and NLTK, it is trained and evaluated on the TMDB 5000 Movie Dataset.

Users receive five movie recommendations based on their selected title if it exists in the dataset; otherwise, they can input custom movie details for suggestions. The system is designed for extensibility and can be enhanced with additional data or user feedback.

Key Features

Personalized Suggestions

Recommends movies based on individual user preferences and selected titles.

Content-Based Filtering

Uses movie metadata and NLP techniques for similarity-based recommendations.

Efficient Data Processing

Handles large datasets efficiently for quick and relevant recommendations.

Extensible Design

Can be improved with more data sources or user feedback for better accuracy.