Problem Statement:

Companies seek to enhance user experience and drive sales by providing personalized and relevant product recommendations to each user

Objective:

Deliver a production-grade pipeline capable of predicting the top recommended products for each user, enabling actionable business decisions and ROI improvements

Process Pipeline:

• User-Item Interaction Data Ingestion
• Collaborative Filtering Data Preparation
• ALS Model Training using Spark ML
• Real-time Recommendation API Deployment
• Recommendation Quality Monitoring

Technologies & Models:

ALS Collaborative Filtering, Azure ML, Azure Databricks, Spark ML

Key Outcomes:

Personalized Recommendations, Enhanced User Engagement, Sales Growth