The trinity of luxury fashion recommendations: data, experts and experimentation

Abstract

Farfetch is the leading platform for online luxury fashion shopping. We have more than 3000 brands and high-end designers with the biggest catalog of luxury products available worldwide to more than 1 million customers. The high-end luxury fashion segment where Farfetch operates in is a notably complex and intricate field. Fashion trends change very fast and can come from anywhere, at any time, thus being very hard to capture. Ultimately, people's tastes are very personal and hard to extrapolate. Users of luxury websites have understandably high expectations and demand a high-end, curated and knowledgeable experience in all aspects. To achieve this, the recommendations engine powering the Farfetch platform is being built on top of three main pillars: 1) data, 2) expert knowledge, and 3) experimentation. Data is obviously the core of any automated recommender system. Like many e-commerce platforms, we collect and leverage various implicit interactions by tracking our users' journeys on Farfetch.com and apps, as well as the explicit preferences they often set - such as their favourite designers. From implicit feedback data we started building the state-of-the-art recommender systems based on collaborative approaches only to realize that our catalogue would not allow for item-item collaborative recommenders, since a product's lifetime is either too short with unique pieces being bought as soon as they go live, or too long with some timeless iconic items lasting forever. Hence, we needed to implement hybrid versions of collaborative-based recommenders which emphasized the products' content data [1]. Throughout the experimentation process over these algorithms, both implicit and explicit feedback data seemed to fall short to encode the sense of fashion expected by our customers. The obvious next step was to use the internal knowledge embedded in several teams of fashion experts and stylists. Although not trivial, there are many ways we can leverage this expert knowledge into improving the fashion understanding of our recommender systems: • Our content editors create the editorial pages with the latest trends and write the products' descriptions. This data allows us to build the relationships between designers to create adjacency models and incorporate taxonomy data employing NLP approaches [5]. • Our visual merchandising experts curate crucial listing pages with products respecting business rules, fashion trends and our signature on fashion. This allows us to encode colorflow and style trends by using style transfer techniques such as computing Gram matrices from convolutional feature maps [2]. • Our stylists manually curate outfits respecting Farfetch's style identity. This allows us to build automated outfits based on siamese neural networks on top of Convolutional Neural Networks [3, 4]. In order to tie these sources of information together in a seamless manner, we follow a strict experimentation workflow, where we iterate fast, deliver in a controlled way through AB testing, and track and evaluate the impact in different dimensions. This process has allowed us to optimize the business value of the system in different contexts and gain a better understanding of our customers and what works and doesn't work for them. In this talk, we will share the Farfetched solutions of our journey on building personalized recommendations in the segment of luxury fashion using data, experts and experimentation.