How to build a content recommendation engine with Snowplow

Recommendation systems are pivotal in maximizing user engagement and satisfaction, thus driving growth, and are increasingly viewed as strategic assets by companies. The process of building a content recommendation engine for the Snowplow blog is explored, emphasizing the importance of high-quality event-level data to form a clear view of content performance and generate successful recommendations. The system combines metric-based and personalized strategies, leveraging granular behavioral data to tailor recommendations without relying heavily on personal identifiers, thereby aligning with privacy considerations. The architecture is built on Snowplow's data pipeline, using AWS services such as Redshift and S3 for data processing and storage, and employs SQL-based models for scoring and ranking content based on user engagement metrics. The design ensures flexibility and adaptability, allowing for domain-specific business logic to be embedded into the data pipeline, which enhances the recommendation engine's ability to provide relevant content based on behavioral similarity rather than just user or content similarity. This approach not only improves user experience but also minimizes biases, contributing to a more effective and personalized recommendation system.