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Documentation: AI Recommendation Engine

Overview

This project implements a sophisticated, two-stage recommendation pipeline designed to provide highly relevant suggestions from a large catalog of items. Unlike simple search engines that rely on keyword matching, this system understands the semantic meaning of item content, enabling it to find conceptually similar items and then apply a layer of learned business logic to intelligently rank the results. The architecture is built using a modern microservices pattern, ensuring that each component is decoupled, independently scalable, and maintains a single responsibility. This makes the system robust, maintainable, and ready for production deployment.

Key Features

• Microservice Architecture: The system is broken down into independent services (vectorization, re-ranking, matching) that communicate over a network.
• Semantic Vector Search: Utilizes a deep learning model (sentence-transformers) to convert text-based content into rich numerical vectors. This allows the system to find matches based on meaning and context, not just keywords.
• Two-Stage Recommendation Pipeline:
  • Candidate Generation: A highly-efficient first pass uses a vector database (ChromaDB) to retrieve a broad set of relevant candidates from many items.
  • Intelligent Re-ranking: A second, more computationally intensive stage uses a trained XGBoost model to re-rank these candidates based on a nuanced, learned understanding of what constitutes a “good” recommendation.
• Decoupled & Reusable Feature Engineering: Feature generation logic is centralized in a shared module, ensuring consistency and completely eliminating training-serving skew.