Modern e-commerce platforms rely on intelligent recommendation systems to connect users with relevant products. Traditional keyword-based search systems often fail because they match words rather than meaning.
This project addresses that challenge by combining:
- 🔍 Semantic Search
- 🧠 Transformer-Based Embeddings
- ⚡ Vector Retrieval
- 🎯 Business-Aware Ranking
- 👤 Personalized Recommendations
- 💡 Explainable AI
- 📊 Interactive Analytics
into a unified recommendation platform capable of delivering highly relevant product suggestions.
The architecture is inspired by recommendation systems used by platforms such as:
- Amazon
- Flipkart
- Walmart
- Shopify
- Alibaba
while emphasizing transparency, modularity, and explainability.
The recommendation engine follows five core principles:
Semantic embeddings enable the system to understand user intent beyond exact keyword matching.
Recommendations are generated using a two-stage pipeline:
- Candidate Retrieval
- Multi-Signal Ranking
This mirrors modern industrial recommendation architectures.
Recommendations are not driven solely by similarity.
The engine incorporates:
- Semantic Relevance
- Product Ratings
- Popularity
- Budget Constraints
- Business Quality Signals
Recommendations adapt to different user personas and preferences.
Every recommendation is accompanied by a human-readable explanation describing why it was selected.
User Query
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Sentence Transformer Encoder
all-MiniLM-L6-v2
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Semantic Query Embedding
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FAISS Vector Database
IndexFlatL2 Search
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Top-K Candidate Products
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Multi-Signal Ranking
┌────────────┬────────────┬────────────┐
▼ ▼ ▼ ▼
Similarity Rating Popularity Budget
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Personalization Layer
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Explainability Engine
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Streamlit Dashboard
Traditional search:
Keyword → Match
ARGUS-style retrieval:
Meaning → Retrieval
- Transformer Embeddings
- Context-Aware Search
- Semantic Understanding
- Query Intent Recognition
- Dense Vector Retrieval
all-MiniLM-L6-v2
Embedding Dimension:
384
High-performance nearest-neighbor search powered by FAISS.
- IndexFlatL2 Retrieval
- Fast Top-K Search
- Semantic Similarity Matching
- Scalable Candidate Retrieval
- Millisecond Search
- Efficient Vector Operations
- Production-Ready Retrieval Architecture
Candidate products are ranked using a weighted scoring framework.
| Signal | Purpose |
|---|---|
| Semantic Similarity | Measures query relevance |
| Product Rating | Captures customer satisfaction |
| Popularity | Measures market adoption |
| Budget Match | Ensures affordability alignment |
| Business Score | Incorporates platform priorities |
Final Score =
0.35 × Similarity
+ 0.25 × Rating
+ 0.15 × Popularity
+ 0.10 × Budget Match
+ 0.05 × Business Score
Profile-aware recommendation boosting enables tailored experiences.
Boosts products related to:
- Gaming
- RTX GPUs
- Graphics Performance
- ASUS
- Lenovo LOQ
- Gaming Laptops
Boosts products optimized for:
- Machine Learning
- Deep Learning
- High RAM
- CUDA Support
- GPU Workloads
Prioritizes:
- Affordability
- Battery Life
- Lightweight Devices
- Daily Productivity
Every recommendation includes transparent reasoning.
- Retrieved due to strong semantic relevance
- Highly rated by customers
- Fits specified budget constraints
- Suitable for AI workloads
- Matches selected user profile
This transforms recommendations from:
"Recommended"
to:
"Recommended because..."
Interactive Streamlit dashboard providing:
- Product Search
- Profile Selection
- Recommendation Results
- Explanation Viewer
- Product Distribution
- Rating Analysis
- Recommendation Trends
- Search Statistics
- Ranking Insights
Raw product and review data are cleaned and standardized.
- Missing Value Handling
- Duplicate Removal
- Product Aggregation
- Feature Engineering
- Metadata Processing
Product descriptions are converted into dense semantic vectors using Sentence Transformers.
all-MiniLM-L6-v2Generated embeddings are stored inside a FAISS index.
- Fast Retrieval
- Scalable Search
- Efficient Similarity Matching
User queries are embedded and matched against product vectors.
Top-K Candidate Products
Retrieved products are scored using business and relevance signals.
User-profile boosts are applied.
Recommendation reasoning is generated.
Results are delivered through an interactive Streamlit interface.
The recommendation engine includes standard ranking metrics.
| Metric | Description |
|---|---|
| Precision@K | Relevant items within Top-K results |
| Recall@K | Coverage of relevant products |
| NDCG@K | Ranking quality evaluation |
results/evaluation_report.csv
- Sentence Transformers
- Hugging Face Transformers
- NumPy
- Pandas
- FAISS
- Dense Embeddings
- Semantic Search
- Streamlit Dashboard
- Interactive Analytics
- Matplotlib
- Seaborn
- Unit Testing
- Recommendation Validation
- Ranking Verification
amazon-rec-engine/
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├── analytics/
├── assets/
│ ├── system_architecture.png
│ └── screenshots/
│
├── data/
│ ├── raw/
│ └── processed/
│
├── logs/
├── models/
├── results/
├── src/
├── tests/
│
├── app.py
├── run_pipeline.py
├── requirements.txt
├── README.md
└── LICENSE
- Semantic Search
- Dense Vector Retrieval
- Explainable AI
- Recommendation Systems
- Modular Architecture
- Scalable Pipeline Design
- Reusable Components
- Logging Framework
- Data Processing Pipeline
- Feature Engineering
- Vector Indexing
- Personalized Experiences
- Business-Aware Ranking
- Analytics Dashboard
- Collaborative Filtering
- Hybrid Recommendation Systems
- User Behavior Modeling
- LambdaMART
- XGBoost Ranker
- LightGBM Ranker
- Click Feedback
- Purchase Signals
- Reinforcement Feedback Loops
- Multimodal Search
- Image Retrieval
- Visual Product Matching
- FastAPI
- Docker
- AWS
- Kubernetes
AI & Machine Learning Engineer • Recommendation Systems Enthusiast • Data Science Practitioner
Built to explore:
- Recommendation Systems
- Semantic Search
- Vector Databases
- Explainable AI
- Personalized Ranking
- Production ML Architectures