Building Recommender Systems for Fashion: Industry Talk Abstract

Abstract

There has been a lot of recent interest in building recommender systems for fashion, with increased attention and investment from the retail industry. For academia, the fashion domain presents new challenges and opportunities that have not been explored before. Dressipi is a personalisation and style advice engine for women's fashion. We work with some of the biggest retailers in the UK who have integrated our service into their site, and are currently expanding to the US and Australia. Since our launch in 2011 we have been helping millions of users find the clothes that they will love, buy and keep. In this talk I will discuss the unique characteristics of the fashion domain and some of the most interesting challenges they pose for recommender systems. Fashion is inherently social and public: we dress not only for ourselves but also for the appropriateness of the environment we are in. When a user buys clothes it is not only important that they like the items themselves, but also that they feel confident and comfortable in the situation they are in. Fashion recommendations must satisfy two sometimes competing objectives: identifying the user's personal preference from their past behaviour and giving advice on what changes to their style would make them look better. Unlike other domains, recommendations should not be purely based on the user's personal taste and past activity. They must also take public perception into account by being aware of fashion rules, outfit guidelines and current trends. Many companies providing recommendations in this space have realised that the user-item interaction data alone can only get you so far. We have started gathering additional personal information about the users in questionnaires, if they wish to provide it. Examples of this include body shape, age, favourite colours, lifestyle etc. These additional data points allow for some exciting applications such as giving style advice and generating high quality recommendation reasons that are useful to the user. For example: `A bodycon dress is a figure flaunting style for your slender frame'. The main challenges addressed in this talk are: • Users are looking for guidance and validation that their fashion choices present the best version of themselves. • There are objective fashion do's and dont's that professional stylists know about but users might not. • Trends and popular culture events influence user preference and public perception quickly and sometimes drastically. • Good recommendation reasons are extremely important, especially when trying to give advice and recommend items outside of the user's comfort zone. • Outfits: a big factor in the decision to buy an item is the user's existing wardrobe. It is important for them to know whether they can wear the new item together with garments they already own to create good outfits.