Recommender systems help users find something great for them from among huge numbers of movies, songs or products. For a company like ASOS, this becomes increasingly critical as we want to provide great and frictionless user experiences to customers that help find the right products amongst the 130,00 fashion items available. We will look at the recommendation problem and then develop a simple yet powerful model. We will then propose a way to expose this model as an API. This talk is ideal for people with a basic knowledge of machine learning. It introduces Recommendation Systems alongside TensorFlow2 and TensorFlow Serving.
Starting by looking at recommendation systems in real life, we will look at how they formulate the recommendations problem when users haven’t explicitly said what things they like. We’ll develop an understanding of Learning to Rank, a machine learning problem that’s a bit different from regression or classification. We’ll learn about matrix factorization, the model that won the Netflix prize about 10 years ago. It defines users and items as lists of numbers that describe taste and style.
Equipped with this knowledge, we’ll build a simple recommendation system using TensorFlow2. Students will receive a link to a Collaboratory notebook (which requires having a Google account to access) which will already have some code to import data. We will then develop and experiment. We will then look at exposing model as an API. By the end every student should have trained and served a simple recommender.
Supported by
