Unsupervised Product Matching for E-commerce
Project Overview
Our Product Matching system is designed to efficiently map every item in a client's e-commerce catalogue to those in competitor catalogues without need for any labelled data. This solution is heavily used in e-commerce websites for price tracking, enabling businesses to stay competitive and make data-driven pricing decisions.
Key Challenges
- Handling client catalogues with over 100,000 items
- Processing competitor catalogues exceeding 10 million items
- Developing a fast mapping pipeline to handle large datasets efficiently
- Matching as many attributes as possible for accurate product comparisons
- Ensuring real-time price tracking capabilities across multiple e-commerce platforms
Key Features
- Developed a high-performance product matching algorithm optimized for e-commerce data
- Implemented parallel processing techniques to handle millions of products efficiently
- Created a flexible attribute matching system to maximize the accuracy of product comparisons
- Utilized advanced indexing and caching strategies to improve search and matching speeds
- Designed a scalable architecture capable of handling growing product catalogues
- Integrated real-time price monitoring and alerting system for competitive analysis
Technologies Used
- Distributed computing frameworks for parallel processing of large datasets
- Advanced machine learning algorithms for intelligent product matching
- High-performance databases for efficient data storage and retrieval
- Custom-built indexing system for fast search capabilities
- Real-time data streaming for up-to-date price tracking
- Cloud infrastructure for scalable deployment and processing
Project Status
- Successfully deployed for a US-based startup, serving clients across multiple verticals
- Mapped product catalogues against 70 competitors in industries including Grocery, Furniture, and Automotive
- Increased the efficiency of generating high quality matches by 20x
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