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
Fashion ecommerce faces a challenge similar to music a decade ago. In order to build game-changing tools, the fashion industry needs to understand people, and not just build new algorithms. And 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.
At Chicisimo, our effort consists on digitizing this data, and making it available online. Developing a high-quality dataset is the success factor towards building a human-level tool to offer outfit advice. Data is a critical element in Machine Learning for fashion.
Chicisimo is a learning mechanism. And we love how the future looks on top of this new understanding.
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 fashion 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 fashion 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 fashion 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 fashion 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 a violet sweater requires a deeper understanding of what someone likes and needs. 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
Product first. Building the right product experience is first for us.
It also allows us to learn what is the data that makes an impact and what is the technology that needs to be built, in order achieve our objetive of automating outfit advise.
A few examples: Thinking in terms of product and retention helps us focus on creating user habits. Thinking about the onboarding process helps us overcome the cold-start problem. Understanding product cognitive overhead, leads us to search a specific type of design.
And we love the feedback we receive, and how we are regularly featured as App of the Day by Apple in more than 60 countries in 2017, and being Android’s Best App in Spain and France 2015 and 2016.
Please visit this essay to learn about the process we followed to built our product and data platform.