This project is a recommendation system framework designed for building, evaluating, and operationalizing personalized item suggestion engines. It provides a comprehensive toolkit for implementing collaborative filtering and content-based algorithms, supported by an end-to-end machine learning pipeline for preparing datasets and deploying predictive models.
The framework distinguishes itself through the integration of knowledge graphs to provide richer context for recommendations and the use of industry-specific patterns to accelerate system deployment. It also includes a specialized model evaluation toolkit for measuring recommendation quality through diversity analysis, novelty, and ranking metrics.
The system covers the full development lifecycle, including data engineering for interaction datasets, hyperparameter tuning, and distributed model training across CPU and GPU clusters. It further provides tools for performance benchmarking, API load testing, and model effectiveness tracking via A/B testing and conversion rates.
The project includes command-line utilities for parameterized notebook execution to validate system behavior.
