Shopping AI
Image-based product recommendation engine using deep learning, computer vision, and vector database indexing to find visually similar items across millions of products.
Screenshots
About This Project
Created an image-based recommendation engine for e-commerce platforms that uses deep learning and computer vision to extract visual features from product images and form a high-dimensional vector space. The system indexes millions of product images via Faiss vector database, enabling lightning-fast similarity search to recommend visually similar items to shoppers. When a user views or uploads a product image, the engine instantly retrieves the most similar products from the entire catalog.
Key Features
- Deep learning feature extraction from product images
- Vector space construction for visual similarity representation
- Faiss-based vector database indexing for millions of images
- Near-instant similarity search across massive product catalogs
- Visual recommendation engine for e-commerce platforms
- Scalable pipeline for processing and indexing new products
Challenges & Solutions
- Extracting meaningful visual features that capture product similarity
- Indexing and searching millions of product images efficiently
- Optimizing vector database queries for real-time recommendation
- Handling diverse product categories with varying visual characteristics
Results & Impact
- High-accuracy visual similarity recommendations
- Real-time search across millions of indexed product images
- Improved product discovery and user engagement on e-commerce platforms
Technologies Used
Project Details
- Category
- ai
- My Role
- Senior Research Engineer
- Duration
- 8 months
- Year
- 2021-2022
- Company
- Huawei
- Status
- Internal / Private
Internal Project
This project was developed for internal use at Huawei. Source code and live demo are not publicly available due to confidentiality.