movie and music Recommendation System Recommendation System Using Autoencoders This repository contains the implementation of a recommendation system based on the academic paper:
Ferreira, D., Silva, S., Abelha, A., & Machado, J. (2020). Recommendation system using autoencoders. Applied Sciences, 10(16), 5510.
Table of Contents Overview Objectives Installation Usage Contributing License Overview The recommendation system implemented in this repository is based on autoencoders, which are a type of artificial neural network used for unsupervised learning. This system is designed to effectively calibrate user preferences and provide personalized recommendations, ultimately benefiting both users and businesses.
Objectives The main objectives of this recommendation system are to:
Increase the number of sales Improve the company's revenue Encourage engagement and activity on products and services Gain a competitive advantage by providing better recommendations than competitors Calibrate user preferences to understand their needs and interests Make personalized recommendations that cater to each user's unique preferences Installation To set up the environment and install the required dependencies, please follow these steps:
Clone the repository: bash Copy code git clone https://github.com/yourusername/recommendation-system-autoencoders.git Change to the repository directory: bash Copy code cd recommendation-system-autoencoders Install the required dependencies: Copy code pip install -r requirements.txt Usage To use the recommendation system, follow these steps:
Prepare your dataset in the appropriate format (e.g., CSV, TSV, or JSON). Update the config.yaml file with the relevant information about your dataset and model parameters. Train the autoencoder model: Copy code python train_autoencoder.py Generate recommendations using the trained model: Copy code python generate_recommendations.py For more detailed instructions and examples, please refer to the documentation.
Contributing We welcome contributions to this project. To contribute, please follow these steps:
Fork the repository. Create a new branch with a descriptive name. Make changes or additions to the code. Create a pull request with a detailed description of your changes. For more information on how to contribute, please refer to our contributing guidelines.
License This project is licensed under the MIT License. For more information, see the LICENSE file.