# Delivery Hero's Product Matching System Using LLMs Delivery Hero, a global online food delivery and quick commerce company, developed a sophisticated product matching system to address the challenges of identifying similar products across their inventory and competitor offerings. This case study details their journey in implementing a production-grade LLM-based solution for semantic product matching. ## Business Context and Problem Statement - Delivery Hero needed to identify similar products between their inventory and competitor offerings - The system needed to help develop pricing strategies and understand product variety differences - Additional use case included identifying duplicate items within their own product range - The solution focused on matching products using product titles, with potential for expansion to include images and other attributes ## Technical Implementation Journey ### Initial Approach: Lexical Matching - Implemented basic text matching using bag-of-words approach - Enhanced with Term Frequency (TF) and Inverse Document Frequency (IDF) - Utilized BM25 similarity scoring - Key advantages: - Limitations: ### Advanced Implementation: Semantic Encoder - Utilized SBERT (Sentence-BERT) library - Based on pre-trained transformer LLM - Customization through fine-tuning: - Technical advantages: - Implementation challenges: ### Production Architecture: Retrieval-Rerank System - Implemented two-stage architecture for optimal performance - Stage 1 - Retrieval: - Stage 2 - Reranking: ## MLOps and Production Considerations ### Model Training and Optimization - Implemented hard negative sampling strategy: - Fine-tuning pipeline: ### System Architecture Considerations - Built scalable pipeline handling large product catalogs - Implemented efficient data processing workflows - Balanced system resources between: - Designed for production deployment with: ### Production Deployment Strategy - Implemented staged rollout approach - Created monitoring systems for: - Established feedback loops for continuous improvement - Developed maintenance protocols for: ## Technical Outcomes and Benefits - Successfully identified similar products across different catalogs - Improved pricing strategy development - Enhanced product assortment management - Achieved balance between accuracy and computational efficiency - Created scalable solution for growing product catalog ## Future Enhancements and Scalability - Potential expansion to include: - Planned improvements: ## Key Learning Points - Importance of balanced approach between speed and accuracy - Value of multi-stage architecture in production systems - Critical role of hard negative sampling in model improvement - Necessity of efficient resource utilization in production - Benefits of incremental implementation approach ## Technical Infrastructure - Utilized SBERT for semantic encoding - Implemented transformer-based cross-encoders - Developed custom fine-tuning pipelines - Created efficient data processing workflows - Built scalable production architecture This implementation demonstrates a sophisticated approach to deploying LLMs in a production environment, balancing technical requirements with business needs while maintaining system efficiency and scalability.