“…algorithms are also responsible for fueling restaurant apps. Restaurant recommendation (or “discovery”) apps — from stalwarts like Foursquare to upstarts like MyFab5 — come up with recommendations based on algorithms’ interpretations of data (yours and theirs). Others, like the just-launched Luka and the six-month-old Flavour, apply algorithms to recommendations curated by professionals and tastemakers. But when biases, context, and emotion are removed from the equation, how effective are the algorithms when it comes to recommending something you’d like — especially when it’s regarding something culturally ingrained and intensely personal like food? And how do you find a balance between the two?”
You can see the full article on Eater here.
“We have a data team that is constantly trying to solve the problem of finding the newest and greatest places around the world,” Covington says. “You can imagine the scale of this problem — instead of finding out about cool places with an ear to the ground, we are working on doing it with machine learning.”