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This repository offers a comprehensive suite of models for building a robust movie recommendation system. It explores various recommendation techniques including collaborative filtering, content-based filtering, and matrix factorization. Each approach is designed to enhance the user experience by providing personalized movie suggestions. Detailed d

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ManasiPandit48/Movie-Recommendation-systems

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Movie Recommendation System

This repository contains various models for creating a movie recommendation system. Each model uses different methods to provide accurate and personalized movie recommendations. The main methods included are:

  • Collaborative Filtering: Recommends movies based on user interactions.
  • Content-Based Filtering: Recommends movies similar to those a user has liked in the past.
  • Matrix Factorization: Uses techniques like Singular Value Decomposition (SVD) to uncover latent factors in user-item interactions.
  • Hybrid Methods: Combines multiple approaches to enhance recommendation accuracy.

Each method is implemented in its respective subfolder, with detailed documentation and code examples provided.

Installation

To install the necessary packages, run:

pip install <requirements>

Usage

To run a specific model, navigate to its subfolder and execute the corresponding script. For example:

cd collaborative_filtering
python <main>.py

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This repository offers a comprehensive suite of models for building a robust movie recommendation system. It explores various recommendation techniques including collaborative filtering, content-based filtering, and matrix factorization. Each approach is designed to enhance the user experience by providing personalized movie suggestions. Detailed d

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