Taste graphs: How to understand fashion taste like Spotify does with music

Gabriel Aldamiz...
May 21 · 5 min read

There is plenty of data in the manufacturing and distribution of clothes. But once clothes are sold and people are wearing them, there is nothing. We simply can’t access people’s closets or understand their outfits.

In the following lines, I will share the following:

  • Taste graphs will transform fashion;
  • They will focus on understanding post-purchase clothing behaviour;
  • They will allow tech companies to understand taste, as Spotify does with music;
  • They will end up owning people’s attention, because they will be useful.

Analyzing demand for outfits

The learnings below are based on 4 years analyzing the demand of outfit ideas at Chicisimo, where we’ve built the infrastructure to automate outfit advice.

We’ve focused on two questions: How do people describe their clothes, outfits and what-to-wear needs? How can we learn about what clothes people have in their closets? In order to respond to these questions, we built an ontology of what-to-wear needs and a taste graph, and all the infrastructure to automate outfit advice.

An outfit is a playlist of clothes but at the same time is much more. It is also a correlated list of descriptors: it can be comfy, or perfect for the weekend. An outfit contains correlations among clothes, and more important, the deep meaning that a person assigns to her clothing preferences. Outfits provide a unique perspective into closets.

Taste graphs will allow teams to own people’s attention

The biggest opportunity in fashion technology today is to build a mechanism to understand post-purchase clothing behaviour. And then, build technology on top of that understanding: technology to help people feel better with their clothes.

Traditional tech efforts focus on efficiently selling more clothes to people, and ignore the post-purchase experience. Once the purchase is finished, companies are blind and can’t see what happens next.

Offering a post-purchase experience that helps people feel well with their clothes, will let the winner own people’s attention, and so many more things as a result.

Taste graphs will power a Spotify for fashion

A Spotify for fashion will understand your taste and needs, because it’ll be powered by a taste graph.

It will help you decide what to wear at any time. You’ll be able to easily store your clothes in a virtual closet, and it will put outfits together for you. It will help you plan your outfits depending on your context, and will suggest new clothes that match your wardrobe.

Helping people feel well with their clothes will be the key functionality of such a service. People want to feel well with their outfits. They want to feel confident, comfortable, happy, beautiful, unique, sexy, stylish, powerful. Instead of that, many people feel stressed or bored or tiny. More than about clothes, it’s about wellness.

1.- Capture units of taste data

Before we try to understand taste, we need to understand what type of data we need to focus on. Spotify focuses mostly on playcounts (each time you listen to a song), and a playcount clearly defines your current behaviour.

We have learnt that the units of capturable taste data are related to text and images. Words express a need (“i need ideas to go to the office”). Images of clothes represent the clothes people own, and need help with. There are other units of capturable taste data, but it comes down to text and images. Then, in our mobile app we’ve built different easy-to-use input interfaces to capture data and allow people to communicate with the system.

2.- An Ontology of what-to-wear needs

Fashion has a problem: it lacks a common classification system. The expression of clothing behaviour is very fragmented: text and images have different meanings for each person, and each person expresses the same concept differently. Due to the lack of this classification (or taxonomy), people’s data is noisy and algorithms cannot work with it. To solve this problem, we’ve built a fashion ontology, which is the backbone of our taste graph.

Our ontology has been built to understand how people refer to their what to wear needs and how they describe their outfits. We don’t think it is important to build a taxonomy that describes clothes (there are many teams doing so), so we don’t want to extract 100% of the metadata of a “blue and white striped cotton v-neck shirt with long sleeves”.

The objective of our ontology is to understand people, not to understand clothes. We want to help you decide “how to wear your black dress to go to your friend’s wedding during a cold day”. This ontology allows us to understand people, their behaviour and their needs, and also converts incoming data into clean and structured data, so our algorithms can make use of incoming data. Think of Netflix initial classification system or Google’s synonym matching.

3.- Taste graphs to understand fashion taste

When we get dressed in the mornings, we establish correlations among clothes, and among our ways of describing our outfits and needs. It will become easier and easier for a machine to capture and correlate clothes in an outfit, but the real value is to capture correlations as described by people.

Taste graphs capture those correlations among descriptors, outfits and people. Think of it as a brain that understands “what goes well with” any garment for a particular occasion, etc. It has this understanding because it analyzes hundreds of millions of correlated descriptors, described outfits and queries. Then, it filters them to your specific characteristics and context.

The end game

Taste graphs will provide structured and correlated taste data. And then will allow teams to build personalized and meaningful services for each person. Our closets will be taste graphs connected to ecommerces catalogues (also graphs), and everything will change. Taste graphs will transform fashion.

Thanks for reading!

Gabriel Aldamiz...

Written by

Founder & CEO at @chicisimo. Machine learning for women’s closets. 2018 App of the Day in +60 countries

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