An effective recommendation pipeline for e-commerce involves:

1. Event Tracking: Use Snowplow to track granular user interactions like clicks, searches, views, and purchases in real time.
2. Data Storage: Route enriched events to platforms like Snowflake or Databricks for processing and modeling.‍
3. Feature Engineering: Create behavioral features such as product affinity scores, session history, and item co-occurrence metrics.
4. Model Training: Use collaborative filtering or deep learning techniques to build recommendation models.
5. Inference: Serve predictions via APIs or streaming systems to personalize content or product listings dynamically.

Snowplow provides the behavioral backbone for building rich, real-time user profiles essential to personalized recommendations.