Recommendation engines are one of the most well-known, widely-used and highest value use cases for applied machine learning. Search and recommender systems are closely linked, often co-existing and intermingling. Indeed, modern search applications at scale typically involve significant elements of machine learning, while personalization systems rely heavily on and are deeply integrated with search engines. In this session, I will explore this link between search and recommendations.

In particular, I will cover three of the most common approaches for using search engines to serve personalized recommendation models. I call these the score then search, native search and custom ranking approaches. I will detail each approach, comparing it with the others in terms of various considerations important for production systems at scale, including the architecture, schemas, performance, quality and flexibility aspects. Finally, I will also contrast these model-based approaches with what is achievable using pure search.