Vancouver-based startup Wantering aggregates fashion users will love on its e-commerce discovery platform.

Don’t you wish that e-commerce discovery sites knew what you liked and what you didn’t like? The Vancouver-based startup Wantering helps users find fashion online by showing them products that they will like.

How does Wantering know, you ask? It aggregates users’ likes using a similar algorithm to the one that Netflix uses. The new social discovery start-up is part of startup accelerator GrowLab’s latest class. Designed like a fashion Tumblog with a Pinterest-style layout, Wantering features a continuous stream of product photographs based on shopper preferences including gender, price and type of fashion. Wantering lists the price and designer, and links to an external site where the item can be purchased. As users rate items, the system learns what users like and recommends items based on their ratings.

“We’re also working on some personalization magic to help cut through the noise even further,” said Nick Molnar, co-founder of the company. “We take all the action happening across the social web and boil it down into something you can browse through in under a minute and find something you want to buy.”

Currently, Wantering is simply a collection of popular fashion, not an ecommerce site. Molnar said that plans are in the works for working with retailers in the future.

“We use Tumblr, blogs, Svpply, and Polyvore data to figure out what’s hot,” Molnar told Betakit. “We’re working on expanding our data sources even further. We think having the best trend and popularity data is the key to our success.”