Production-Grade E-Commerce Recommendation System: Full-Stack Practice Integrating Collaborative Filtering, Content Matching, and Learning to Rank

This article introduces an open-source production-grade e-commerce recommendation engine project. The project uses a multi-strategy fusion architecture, combining core technologies such as collaborative filtering, content-based filtering, LightGBM learning to rank, and FAISS approximate nearest neighbor search, to fully implement the multi-stage pipeline architecture of industrial-grade recommendation systems. The project not only demonstrates the core technical implementation of recommendation systems from platforms like Amazon and Netflix but also features cold start handling and diversity optimization, making it a valuable resource for recommendation system learners and practitioners.

Recommendation systems are one of the core competencies of modern e-commerce platforms. From Amazon's "Customers who bought this item also bought" to Netflix's personalized movie recommendations, all rely on complex algorithms and architectures. This project is maintained by Rosm140 and published on GitHub (link: https://github.com/Rosm140/DSA-E-Commerce-Product-Recommendation-Engine) on June 8, 2026, aiming to provide a production-ready recommendation system solution.

The system adopts a multi-stage recommendation pipeline architecture: Data Source → Data Loading → Feature Storage → Candidate Generation → Ranking → Post-Processing → API. Core technologies include:

1. Collaborative Filtering: Based on user-user cosine similarity;
2. Content Filtering: TF-IDF + FAISS ANN search;
3. Learning to Rank: LightGBM LambdaRank model (directly optimizes NDCG metric);
4. Cold Start Handling: Popularity scoring based on weighted heap;
5. Diversity Optimization: Maximum Marginal Relevance (MMR) algorithm;
6. Tech Stack: Backend Python 3.11 + FastAPI, Machine Learning LightGBM/scikit-learn, Vector Retrieval FAISS, Database SQLite, Frontend React 18, etc.

The project has established a comprehensive offline evaluation system, using metrics such as Precision@K, Recall@K, NDCG@K, Hit Rate@K, and Coverage to measure recommendation quality from multiple dimensions. The system provides RESTful APIs with key endpoints including:

• POST /recommend/user: Personalized recommendation;
• POST /recommend/similar: Similar product recommendation;
• POST /recommend/category: Category popular recommendation;
• POST /evaluate: Evaluate recommendation quality. API documentation can be accessed via the interactive interface automatically generated by FastAPI.

This project fully presents all aspects of a production-grade recommendation system, from data access to frontend display. It is not just a stack of algorithms but also reflects systematic architectural design and engineering implementation. For learners in the recommendation system field, it allows in-depth understanding of the practical application of core technologies such as collaborative filtering and learning to rank; for practitioners, its modular design can be directly applied to real projects. In conclusion, this is a rare complete case that provides important references for the study and practice of recommendation systems.