Machine Learning Model for Q&A Ranking

A comprehensive implementation of ML systems for Q&A ranking and promotion forecasting, designed for platforms like Quora, Reddit, and e-commerce sites.

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• https://youtu.be/C842vFY5kRo?si=-mz8ItjKptoXR4-8
• 

🚀 Quick Start

Running Q&A Ranking

cd examples
python run_qa_ranking.py

Running Promotion Forecasting

cd examples
python run_promotion_forecasting.py

📁 Project Structure

.
├── qa_ranking/              # Q&A Ranking System
│   └── qa_ranker.py        # Core ranking logic
├── promotion_forecasting/   # Promotion Forecasting System
│   └── promo_forecaster.py # Core forecasting logic
├── examples/                # Example usage scripts
│   ├── run_qa_ranking.py
│   └── run_promotion_forecasting.py
├── notes.md                 # Detailed technical notes
├── README.md
└── LICENSE

🎯 Features

Q&A Ranking System

• Text Similarity: Jaccard-based semantic matching
• Interaction Metrics: Upvotes, CTR, impressions
• Quality Filtering: Spam detection for short/low-quality answers
• Scalability: Designed for millions of Q&A pairs with <100ms latency

Example:

from qa_ranking.qa_ranker import QARankingSystem

ranker = QARankingSystem()
question = "What is machine learning?"
answers = [
    {"text": "ML is a subset of AI...", "upvotes": 100, "impressions": 500},
    {"text": "I don't know", "upvotes": 0, "impressions": 10}
]
ranked = ranker.rank_answers(question, answers)

Promotion Forecasting System

• Item Similarity: Coverage-based matching (|A ∩ B| / |B|)
• Seasonality Boost: 20% boost for same-month promotions
• Cold Start Handling: Fallback to historical averages
• Data Architecture: Designed for Hadoop/NoSQL storage

Example:

from promotion_forecasting.promo_forecaster import PromotionForecaster

forecaster = PromotionForecaster()
items = ["iphone14", "samsung_s23", "macbook"]
predicted_sales, details = forecaster.predict_sales(items, "2025-06-15")

📊 Evaluation Metrics

Q&A Ranking

• Offline: NDCG, MRR, F1-Score, AUC
• Online: Click-Through Rate (CTR), A/B Testing

Promotion Forecasting

• Accuracy: MAPE, RMSE
• Business Metrics: Revenue impact, forecast vs. actual

🛠️ Technical Details

Q&A Ranking Algorithm

1. Text Processing: Tokenization, lowercasing, punctuation removal
2. Similarity Calculation: Jaccard index between question and answer
3. Scoring Formula:

   Score = 0.6 × Similarity + 2.0 × CTR + 0.1 × log(Upvotes + 1) + Penalty
   

4. Quality Filter: -0.5 penalty for answers with <5 words

Promotion Forecasting Algorithm

1. Item Similarity: Similarity = |Historical_Items ∩ Current_Items| / |Current_Items|
2. Seasonality: +0.2 boost if promotion month matches historical month
3. Cold Start: Use average of all historical sales if similarity = 0

📚 Use Cases

Q&A Platforms

• Quora: Rank answers to maximize user engagement
• Reddit: Sort comments by relevance
• Stack Overflow: Prioritize helpful answers

E-commerce

• Amazon: Forecast promotion performance
• Flipkart: Plan seasonal campaigns
• Walmart: Optimize inventory for promotions

🔧 Requirements

• Python 3.7+
• No external dependencies (uses only standard library)

📖 Documentation

See notes.md for detailed technical documentation including:

• System design considerations
• Feature engineering strategies
• Model selection rationale
• Deployment architecture

📄 License

MIT License - see LICENSE file for details

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

📧 Contact

For questions or feedback, please open an issue on GitHub.

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📜 License

Licensed under the MIT License - Feel free to fork and build upon this innovation! 🚀

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