Real-Time Retail Intelligence: Building E-Commerce Recommendations with Lakebase and AI Search on Databricks

This post details the implementation of an e-commerce recommendation system using Databricks. It covers real-time data ingestion from clickstream data into the Lakehouse in seconds, two distinct low-latency serving paths for recommendations (one leveraging Lakebase Postgres for immediate access and another for more complex model-based recommendations), strategies for handling the cold-start problem with techniques like content-based filtering and hybrid approaches, and the model lifecycle management for continuous improvement of recommendation models. The system integrates AI Search for enhanced product discovery and leverages Databricks' capabilities for scalable data processing and model serving.