Recommendations: They’re in fashion

Abstract

Farfetch, the leading online platform for luxury fashion, has spent several years developing a recommender system. In fact, recommendations have been quite successful in improving both the user experience and the company’s own business metrics [3–9]. In this talk we will shed some light on how we built our recommender system at Farfetch, the main obstacles we faced, and some plans for the future. Recommendations started their journey at Farfetch somewhere around 2015. At the time, we had a single model that trained once per day that updated the users’ recommendations with the same frequency. Currently, we have around 20 models in production and the majority of them are designed to handle streaming data from the users and adapt in realtime to user actions. How can we balance training and improving existing models, creating new models, serving them in real time and still keep our code in check, our tests up to date and our pipelines moving? We will discuss the three main components that we created in order to tackle our real world issue of providing ever-improving recommendations to our customers: The Gym, The Recommenders and The API.