CineMatch-AI-Movie-Recommender

• Overview
• Installation
• Usage
• Dataset
• Model
• Predictions
• Contributing
• License

Overview

This project implements a content-based filtering recommender system using a neural network to recommend movies. The system leverages movie features like genres and titles to provide personalized recommendations, addressing the cold-start problem and enhancing user engagement on streaming platforms.

Installation

1. Clone the repository:

   git clone https://github.com/amritBskt/CineMatch-AI-Movie-Recommender.git
   cd CineMatch-AI-Movie-Recommender

2. Create a virtual environment and activate it:

   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

1. Navigate to the notebooks directory:

   cd notebooks

2. Launch Jupyter Notebook:

   jupyter notebook

3. Open and run the CineMatch.ipynb notebook to see the implementation and results.

Dataset

The dataset used in this project is derived from the MovieLens ml-latest-small dataset.

Model

A neural network is implemented using TensorFlow to perform content-based filtering. The model is trained to predict user ratings based on movie features.

model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(1)
])

model.compile(optimizer='adam', loss='mse')

Predictions

The notebook includes methods to generate personalized movie recommendations for both new and existing users and to identify similar movies.

def predict_for_new_user(user_preferences):
    return model.predict(user_preferences)

def predict_for_existing_user(user_id):
    user_data = ratings[ratings['userId'] == user_id]
    return model.predict(user_data)

def find_similar_items(movie_id):
    movie_data = movies[movies['movieId'] == movie_id]
    return model.predict(movie_data)

License

This project is licensed under the MIT License. See the LICENSE file for details.