Machine learning to understand clothing habits

Chicisimo develops technology to understand the clothing habits of each individual. In order to do this, we’ve built a data platform which is connected to a live environment, our consumer app.

Chicisimo’s main asset is its Social Fashion Graph, a mechanism to classify any type of input (expression of taste) and to capture correlations among inputs (how needs, outfits and people interrelate). On top of this clean and correlated data, we’ve built a search engine for outfit ideas, recommendation technology and we’ll be able to personalize content based on personal characteristics.

The backbone of the Social Fashion Graph is our ontology, a unique list of the world’s what to wear needs. This ontology learns from people’s input.

And then, our mobile app. It has one major objective: to explore interfaces that enable people to provide their input in a meaningful way. The interface evolves rapidly together with the new possibilities provided by our ontology and Social Fashion Graph.

The Social Fashion Graph, our mobile app, and our IP portfolio, are the founding technology assets towards our goal of automating online clothing services.

The evolution of the music sector can explain the future of fashion

The way we enjoy our clothes and find new ones is going to change. The fundamentals of this change can be explained by looking at the music sector.

In the old days, we used radiocassettes to listen to music. We did not have an online service that could understand our taste and help us find new music. “Discovery” was limited. Years later, Audioscrobbler was born and built a mechanism for us to express our taste, by tracking the songs we played. These data we started to produce was then matched against online databases of songs that were also being built then. This new data allowed product builders to create new discovery experiences, giving birth to online music as we know it today.

Availability of people’s taste, and classification of content. At Chicisimo, we love how the future looks on top of this new understanding.

We are creating a high-quality, vertical dataset, by digitizing offline data

The data required to understand people is, today, mostly offline. What I have in my closet, what I am wearing now, what my context is. This data is also very disconnected to how fashion describes clothes.

At Chicisimo, our effort consists on digitizing this data, and making it available online.

The Social Fashion Graph

The objective of the Social Fashion Graph is to provide structure to the data generated by people. And the objective of this structured data is to enable automated and personalized services.

The Social Fashion Graph has two fundamental tasks: (i) classify input: it converts any expression of taste into structured data, regardless of how the input was expressed; (ii) capture correlations among data: it captures how needs, outfits and people interrelate.

As a result of the above, the Social Fashion Graph produces clean and correlated data, a well curated and growing clothing dataset that learns from the real world. This structured data is an enabler of services. As of today, we have built a search engine for outfit ideas, recommendation technology and we’ll be able to personalize content based on personal characteristics.

The backbone of the Social Fashion Graph is our ontology of the world’s what-to-wear needs. It summarizes and gives structure to what to wear needs, as expressed by people.

We think the ontology is pretty unique, and here is why: The traditional fashion taxonomies are a list of tags describing garment characteristics. This approach is understandable, because it is looking at clothes through the lense of other products it has worked on before (songs, books, electronics…). These other lines of products had pre-defined databases, obvious matching systems, and a type of metadata much closer to the way people describe those products.

We have learnt that what to wear needs go well beyond garment-related metadata, and of course these needs are expressed in very different ways. In the age of deep learning and substancial algorithmic advances, next generation fashion taxonomies are transparent and capture the world through the eyes of people, not through the eyes of the industry.

This social, bottom-up approach contributes to smarter algorithms. It also expands the discovery experience: it empowers shoppers to look at each other to decide what to wear and what to buy, instead of having to look at the traditional gatekeepers.

“I need ideas to wear this violet sweater to class tomorrow”

Did you know that most people combine violet sweaters with black pants, black boots or black blazers? Also, violet sweaters are more common during fall/winter than during spring/summer. And they tend to be worn during casual settings rather than formal.

Data is cool. But being able to recommend the right outfit for the violet sweater you have in your closet, requires a deeper understanding of what someone likes and needs, and the ability to match input to output. It also requires creating the right environment for people to express their needs, where inspiration and discovery can take place.

The above is specifically what we are building.

Gracias por crear esta app, me está ayudando a disfrutar mi ropa cada vez más
Sentirme guapa hace que me sienta más segura. Chicisimo me ayuda a conseguirlo
Me encanta ayudar a otras mujeres con mi selección de ideas para vestir

Creating the interface for people to provide their input

In our opinion, one of the obstacles towards personalization in fashion, is product related. How can we create interfaces for people to provide their input?

This is one of the most important objectives of our mobile app: to build the right interface where people can provide their input, and then receive a relevant output. Interestingly, interfaces are 100% dependant on the technology and data behind the content, and obviously on the ability to truly understand the user problem.

In our case, the interface evolves rapidly together with the possibilities provided by our ontology and Social Fashion Graph. And we build the two by exclusively focusing on the very specific problem that our users need to solve.

If you haven’t, play with our app with this in mind.

Obviously, we love the feedback we receive, and how we are regularly featured by Apple as App of the Day in more than 60 countries, and being Android’s Best App in 2015 and 2016.

Fashion. 10 years from now

A decade ago, we did not have Spotify. A decade from now, we will have several “Spotify for clothes”. Closet apps that have strong meaning for us, and help us in very specific ways. There are some specific use cases we like, that fit well together:

  • Automating outfit adviceAmazon, Alibaba, Chicisimo
  • Image understanding (Most ML efforts are here)Wide-Eyes,, ViSenze
  • Systems to match outfits and shoppable productsInstagram, Pinterest, Amazon, Chicisimo
  • Discovery of clothesAmazon, Zalando, Asos, Chicisimo

If the above makes sense to you, please

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