Recommender Systems, Not Just Recommender Models

One of the biggest challenges facing people new to building recommender systems is the lack of understanding around what these systems look like in the real world. The majority of the online content around recommender systems focuses on models and is often limited to a simple example of collaborative filtering. For new practitioners, there is an enormous gap between examples of simple models and a production system that serves recommendations. In this blog post we'll share a pattern that we feel covers the majority of recommender systems deployed today with examples from companies like Meta, Netflix, and Pinterest. This pattern is central to how we think about building end-to-end recsys within the NVIDIA Merlin team and we're excited to share it with the broader community and help build an understanding and consensus of what recommender systems (not just models) look like in production.