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How Etsy taught style to an algorithm

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Building great search features for an e-commerce site is never simple. If it were, the results we get would feature a lot more irresistible gems—and fewer items we’d never buy.

At Etsy, the search challenge is particularly tough. The site’s stock in trade is not the sort of mass-produced goods that can be neatly categorized. Instead, 75% of the 60 million items that its 2 million merchants offer are handmade and therefore one of a kind. Even if they speak deeply to a shopper, they may do so for reasons that are difficult to divine from search terms and the information in product listings.

“We don’t have merchandisers entering the descriptions of the blue shirts in the pallets in the warehouse,” says Mike Fisher, Etsy’s CTO. That means that applying standard search technology to the wealth of products on the site produces results that are . . . well, generic. “If we put a bag in, it shows us different bags,” Fisher says. “Bags, bags, bags.”

As it considered how to improve its search, Etsy concluded that it would be ideal to show shoppers items that not only matched their search terms but appealed to their particular aesthetic preferences. That would increase the chances that they’d love what they saw, which would be a boon for buyers and sellers alike. But doing so would require that the company teach its search engine to understand style.

After about a year of work, Fisher says, Etsy has trained a machine-learning model to effectively suss out the styles of items on the site, based on both textual and visual cues. The company is about to start testing results based on this new algorithm on the Etsy site. But it also believes that the technology it’s developed could have applications well beyond making ecommerce more relevant. Which is why three of Etsy’s data scientists have........

© Fast Company