Welcome file Welcome file

Movie Recommendation Service

This repository contains the code and resources for a movie recommendation system built using TF-IDF vectorization and cosine similarity. The system is designed to recommend movies to users based on their preferences and a provided movie title. This README provides an overview of the project, its components, and how to set it up.

Table of Contents

• Project Overview
• Files and Components
• Docker Hub Availability
• Getting Started
• Usage
• Contributing
• License

Project Overview

The Movie Recommendation Service is a content-based recommendation system that suggests movies similar to a given movie title. It utilizes TF-IDF vectorization to process movie data and calculates cosine similarities to identify similar movies. The service provides both a Python API and a Flask web application to make movie recommendations accessible to users.

Files and Components

making_models.py

• Responsible for preprocessing the movie dataset, applying TF-IDF vectorization, and calculating cosine similarities to build the movie recommendation model.
• Functions and Steps:
  • combineFeatures(row): Combines different features of a movie (genres, director, keywords, cast, popularity) into a single string.
  • Reads movie data from a CSV file (movie_dataset.csv).
  • Fills any missing values in specified features with empty strings.
  • Combines specified features into a new feature called combinedFeatures.
  • Applies TF-IDF Vectorization on the combinedFeatures.
  • Computes cosine similarities between movies based on TF-IDF vectors.
  • Saves the cosine similarity matrix to a file (movie_similarity.pkl) using pickle.

predict.py

• Uses the trained model and processed data to recommend movies similar to a provided movie title.
• Functions and Steps:
  • recommend_movies(movie_title, number_of_movies): Recommends movies similar to the given movie title.
  • Loads the movie dataset.
  • Loads the TF-IDF vectorizer and cosine similarities from the saved model (movie_similarity.pkl).
  • Finds the closest match to the provided movie title using fuzzy matching.
  • Calculates similarity scores with other movies based on cosine similarities.
  • Returns the closest match (provided movie title) and recommended movies.

web_app.py

• Sets up a Flask web application to provide a user interface for accessing movie recommendations.
• Endpoints:
  1. POST /movie: Receives a JSON payload with a movie name and the number of recommended movies. Calls the recommend_movies function from predict.py to generate movie recommendations. Returns the recommendations as JSON.
  2. GET /movierecomend: Receives movie and counter parameters as query parameters. Calls the recommend_movies function from predict.py to generate movie recommendations. Returns the recommendations as JSON.

Docker Hub Availability

The Movie Recommendation Service is available as a Docker container, making it easy to deploy and use in various environments. Whether you're a developer exploring the code or a user interested in the service, Docker offers a convenient way to run the Movie Recommendation Service.

Docker Image

You can find the Docker image for this project on Docker Hub. To run the Movie Recommendation Service as a Docker container, use the following command:

docker run -p 8888:8888 hosseinhimself/recommendersystem

After starting the container, the service will be accessible at http://localhost:8888. For details on using the service and making movie recommendations, refer to the Usage section.

Getting Started

To get started with the Movie Recommendation Service, you need to set up the project environment and run the service. Follow these steps to get started:

1. Clone this repository to your local machine:

   git clone <repository-url>

2. Navigate to the project directory:

   cd Movie-Recommendation-Service

3. Set up a Python virtual environment:

   python3 -m venv venv

4. Activate the virtual environment:

   source venv/bin/activate

5. Install the required Python packages:

   pip install -r requirements.txt

Usage

Running the Movie Recommendation Service

To run the Movie Recommendation Service, follow these steps:

1. Start the Flask web application:

   python web_app.py

   The service will be accessible at http://localhost:8888.

Making Movie Recommendations

You can make movie recommendations using the provided endpoints:

1. POST /movie: Send a JSON payload with a movie name and the number of recommended movies to get recommendations.

   Example usage with cURL:

   curl -X POST -H "Content-Type: application/json" -d '{"movie_name": "example_movie", "movie_numbers": 5}' http://localhost:8888/movie

2. GET /movierecomend: Use query parameters for the movie title and the number of recommended movies.

   Example usage in a web browser:

   http://localhost:8888/movierecomend?movie=example_movie&counter=5
   

Contributing

Contributions to this project are welcome. If you have ideas for improvements or want to report issues, please create a GitHub issue or submit a pull request.