E‑Commerce Recommendation Engine

A scalable recommendation engine built with PySpark and MLFLow on Databricks, designed for a fictional fashion & beauty brand. It uses collaborative filtering and product embeddings to deliver personalized product suggestions and improve customer engagement.

🎯 Problem Statement

1. Information overload Customers struggle to find relevant items among thousands of SKUs.
2. Low conversion rates Generic browsing experiences fail to resonate, hurting sales.
3. Poor user retention Lack of personalization fails to incentivize repeat visits.
4. High return rates Irrelevant recommendations lead to dissatisfaction and returns.

💡 Business Objectives

• Boost Average Order Value (AOV): Suggest complementary or upsell products.
• Increase customer satisfaction: Provide relevant, engaging recommendations.
• Reduce churn: Personalized experiences encourage repeat shopping.
• Improve inventory utilization: Promote less popular items to balance demand.

🛠 Key Features & Architecture

1. Data Ingestion & ETL

   • Raw purchase logs (user‑item‑rating) ingested via Spark pipelines.
   • Transformed and cached for model training.

2. Collaborative Filtering (ALS)

   • Spark ML’s Alternating Least Squares for scalable recommendations.
   • Handles sparse interactions typical of retail datasets.

3. Content-based / Hybrid Extensions

   • Incorporates product metadata (descriptions, categories) using embeddings.
   • Enables recommendations for new (cold-start) items.

4. Model Tracking with MLflow

   • Stores training artifacts, performance metrics, and parameters.
   • Facilitates A/B testing and model governance.

5. Deployment on Databricks

   • Production-ready jobs schedule pipelines and inference endpoints.
   • Supports near-real-time feature generation and scoring.

📈 Business Benefits

Goal	Outcome
Personalized discovery	Customers receive relevant item suggestions
Higher engagement	Increased click-through and conversion rates
Enhanced product exposure	Broader catalog visibility and lower returns
Scalable infrastructure	Engine scales with data, reducing latency

⚙️ Getting Started

1. Clone repo

   git clone https://github.com/Lwhieldon/E-CommerceRecommendationEngine.git
   cd E-CommerceRecommendationEngine

2. Setup Databricks & MLflow

   • Launch workspace, configure cluster, attach repo.
   • Install Python dependencies via requirements.txt.

3. Run pipeline notebooks

   • 01_data_prep.ipynb: ETL and data preprocessing
   • 02_model_train.ipynb: ALS training, hyperparameter tuning, MLflow logging
   • 03_inference_service.ipynb: Score and generate recommendations

4. Deploy to production

   • Convert notebooks to jobs on Databricks, schedule via MLflow/Jobs UI.
   • Expose RESTful API for real-time recommendations.

✅ How to Evaluate

• Offline metrics: RMSE, Precision@K, Recall@K.
• A/B tests: Compare CTR and AOV with control.
• Business KPIs: Measure reclamation rate, repeat purchase frequency.

🚀 Next Steps

• Integrate transformer-based text embeddings to enhance product content understanding ([github.com][1], [linkedin.com][2], [medium.com][3], [blog.adnansiddiqi.me][4]).
• Add session-based modeling (e.g. via graph or embedding techniques) similar to recent Amazon co-purchase systems .
• Deploy front-end widget to surface real-time recommendations on site.
• Implement feedback loop to refine models based on live interactions.

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📞 Contact

Lee Whieldon – Data Strategist & Engineer 📍 Baltimore, MD · github.com/Lwhieldon

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References

1: https://github.com/oaslananka/E-Commerce-Recommendation-System?utm_source=chatgpt.com "oaslananka/E-Commerce-Recommendation-System - GitHub"

2: https://www.linkedin.com/pulse/predicting-your-needs-recommendation-systems-e-commerce-qeccf?utm_source=chatgpt.com "Predicting Your Needs: Recommendation Systems in E-commerce"

3: https://medium.com/the-ai-guide/e-commerce-recommendation-engine-with-collaborative-filtering-cb19cd542c18?utm_source=chatgpt.com "E-Commerce Recommendation Engine with Collaborative Filtering"

4: https://blog.adnansiddiqi.me/building-an-e-commerce-product-recommendation-system-with-openai-embeddings-in-python/?utm_source=chatgpt.com "Building an E-commerce Product Recommendation System with ..."