Slack, as a product, presents many opportunities for recommendation, where we can make suggestions to simplify the user experience and make it more delightful. Each one seems like a terrific use case for machine learning, but it isn’t realistic for us to create a bespoke solution for each.
In the talk, we’ll dive into the Recommend API, a unified framework the team built over the years that allows us to quickly bootstrap new recommendation use cases. Behind the scenes, these recommenders reuse a common set of infrastructure for every part of the recommendation engine, such as data processing, model training, candidate generation, and monitoring. This has allowed us to deliver a number of different recommendation models across the product, driving improved customer experience in a variety of contexts.