A closer look at the AI behind course recommendations on LinkedIn Learning, Part 1

Over the last few years, the team has built the course recommendation engine from the ground up and evolved it to serve recommendations using hyper-personalized models that learn billions of coefficients for our millions of members (Shivani Rao et al CIKM 2019, Polatkan et al blog post). A key goal of this recommendation engine is to surface the most relevant and personalized course recommendations, which can help learners develop new skills and drive engagement on the platform. In this two-part series, we'll show how Learning AI is recommending relevant courses to our members and helping drive engagement by using state-of-the-art AI technologies. In part 1, we'll share an overview of our recommendation engine design and then present a high-level explanation of the three main components of the engine. Later, in part 2, we'll delve deeper into each of the engine's components, providing insight into how we generate personalized course recommendations for every learner on the platform.