Hands-On AI E-Commerce Recommendation System: Intelligent Shopping Assistant Based on Semantic Search and Vector Embedding

This project is a modern AI e-commerce recommendation system integrating semantic search, vector embedding, intent recognition, and conversation memory, aiming to solve the pain point of traditional e-commerce search that relies on keyword matching and cannot understand users' real intentions. The system is built with FastAPI and Sentence Transformers, supports natural language interaction, and can accurately capture the scenario needs behind users' queries. The project comes from the GitHub open-source repository ai-commerce-platform, maintained by Palak Verma.

Original Author and Source

• Original Author/Maintainer: Palak Verma (@plk0309)
• Source Platform: GitHub
• Original Title: ai-commerce-platform
• Original Link: https://github.com/plk0309/ai-commerce-platform
• Release Date: 2026-06-15

Core Issues

Traditional e-commerce search systems rely heavily on keyword matching and cannot understand the real intent of users' queries. For example, when a user searches for 'wireless headphones suitable for the gym', traditional systems only match the keyword 'wireless headphones' and ignore the scenario requirement of 'gym use'. The goal of this project is to build an AI shopping assistant that can understand semantic meaning and support natural language interaction.

Technical Architecture

1. Semantic Vector Embedding Layer: Convert products into dense vectors using the all-MiniLM-L6-v2 model from Sentence Transformers to capture semantic meaning.
2. Efficient Vector Retrieval: Use FAISS to index product embeddings for fast large-scale nearest neighbor search.
3. Intent Recognition Engine: Supports intent types such as search, budget filtering, brand filtering, product comparison, follow-up queries, etc.
4. Entity Extraction System: Extract structured constraints like budget and category from queries (e.g., extract budget 1500 from 'wireless headphones under 1500 yuan').
5. Intelligent Re-Ranking Algorithm: Final Score = 0.5 × Semantic Similarity + 0.3 × Product Rating + 0.2 × Product Popularity.

Technology Stack

Layer	Technology Selection
Backend Framework	Python + FastAPI + Uvicorn
Machine Learning	Sentence Transformers (MiniLM)
Vector Search	FAISS
Data Processing	NumPy + Pandas
API Documentation	Swagger UI

Project Structure

Adopts a modular design. Core components include data loader, embedding generator, search module, ranking engine, and recommender. The outputs include product embedding vectors, data frames, and FAISS index files.

Conversation Memory Function

Supports multi-turn dialogue context understanding: When a user first asks 'wireless headphones under 2000 yuan' and then asks 'show cheaper ones', the system will remember the budget constraint and adjust the price range.

API Usage Examples

The system provides a semantic search endpoint and a filtered recommendation endpoint. The recommendation interface supports passing in queries, session IDs, and return counts. The response includes recognized intents, extracted entities, and a list of recommended products. The service runs on local port 8000 by default and provides automatically generated API documentation via Swagger.

• Semantic Search: Understand natural language queries, beyond keyword matching
• Intent Recognition: Automatically detect multiple query intents
• Entity Extraction: Extract structured information like budget, brand, category
• Intelligent Re-Ranking: Multi-factor hybrid scoring mechanism
• Conversation Memory: Support context-aware follow-up queries
• Keyword Filtering: Avoid irrelevant products
• FastAPI Integration: Asynchronous framework + automatic API documentation

The project roadmap includes:

• Hybrid search (keyword + vector search)
• User personalized recommendation
• Analysis dashboard
• LLM-based recommendation explanation
• Clickstream analysis
• Recommendation feedback loop
• Production deployment and monitoring

This project demonstrates a complete AI e-commerce recommendation system architecture from semantic understanding to vector retrieval, intelligent ranking, and conversation management, serving as an excellent reference implementation for developers to build modern recommendation systems. Core insight: Future e-commerce search will evolve from 'keyword matching' to 'intent understanding', and vector embedding and semantic search are key technologies to achieve this transformation.