Movie Recommendation System

This project implements a movie recommendation system using data from Rotten Tomatoes. The system utilizes a K-Nearest Neighbors (KNN) algorithm based on cosine similarity to provide personalized movie recommendations based on user reviews and ratings.

Table of Contents

• Project Description
• Installation
• Usage
• Data Sources
• Functionality
• License

Project Description

The movie recommendation system processes movie and review data from Rotten Tomatoes, normalizes the scores from various formats, and constructs a user-item review matrix. It employs a KNN model to suggest movies similar to a specified title based on critic reviews.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/movie-recommender.git
   cd movie-recommender

2. Install required packages: Make sure you have Python installed. Then, you can install the necessary libraries using pip:

   pip install numpy pandas matplotlib scikit-learn

3. Download the datasets: Ensure you have the following CSV files in the project directory:

   • rotten_tomatoes_movies.csv
   • rotten_tomatoes_movie_reviews.csv

Usage

You can run the movie recommendation engine by executing the script. Here’s an example of how to use the movie_recommender_engine function:

from movie_recommender import movie_recommender_engine, movie_dataset, review_matrix, cf_knn_model

# Get recommendations for the movie "Twilight"
recommendations = movie_recommender_engine("Twilight", movie_dataset, review_matrix, cf_knn_model, 10)
print(recommendations)

Data Sources

• Rotten Tomatoes Movies Dataset
• Rotten Tomatoes Movie Reviews Dataset

Functionality

1. Data Cleaning:

   • Removes entries with missing movie titles or scores.
   • Normalizes various scoring formats into a consistent scale (0-10).

2. Data Merging:

   • Merges movie details with ratings based on the movie ID.
   • Removes duplicate entries from the merged dataset.

3. Recommendation Engine:

   • Constructs a review matrix with critics as rows and movie IDs as columns.
   • Implements a KNN model to find similar movies based on critic ratings.
   • Returns the top N recommended movies based on a specified movie title.