RePlay is an advanced framework designed to facilitate the development and evaluation of recommendation systems. It provides a robust set of tools covering the entire lifecycle of a recommendation system pipeline:

🚀 Features:

• Data Preprocessing and Splitting: Streamlines the data preparation process for recommendation systems, ensuring optimal data structure and format for efficient processing.
• Wide Range of Recommendation Models: Enables building of recommendation models from State-of-the-Art to commonly-used baselines and evaluate their performance and quality.
• Hyperparameter Optimization: Offers tools for fine-tuning model parameters to achieve the best possible performance, reducing the complexity of the optimization process.
• Comprehensive Evaluation Metrics: Incorporates a wide range of evaluation metrics to assess the accuracy and effectiveness of recommendation models.
• Model Ensemble and Hybridization: Supports combining predictions from multiple models and creating two-level (ensemble) models to enhance the quality of recommendations.
• Seamless Mode Transition: Facilitates easy transition from offline experimentation to online production environments, ensuring scalability and flexibility.

💻 Hardware and Environment Compatibility:

1. Diverse Hardware Support: Compatible with various hardware configurations including CPU, GPU, Multi-GPU.
2. Cluster Computing Integration: Integrating with PySpark for distributed computing, enabling scalability for large-scale recommendation systems.

📖 Documentation is available here.

Table of Contents

• Installation
• Quickstart
• Resources
• Contributing to RePlay

🔧 Installation