CEO Alexa Andrzejewski would like you to know some things about Foodspotting: It’s not just for sharing food photos. And it’s not just for foodies.
Andrzejewski has been saying this for a while now, but with the redesigned Foodspotting app launching today, she has a much stronger case. It offers what she calls a “Pandora-like” interface for recommendations, hopefully making it easier for users to find the food that they’ll like.
The redesigned app is launching for iPhone, Android, and for the first time, on BlackBerry. (Andrzejewski says the Windows Phone app, since it was released more recently, already incorporates some of these principles.) The interface allowing users to rate foods has been simplified and made more prominent. When you see a dish, with just a couple of taps you can say that you “tried it”, that you “loved it”, or that you “want it” (essentially bookmarking the dish to try later). In the pursuit of this simplicity, Foodspotting has also eliminated one of its more charming features, the ability to say you “nommed” a dish — people had a hard time figuring out what exactly that meant, Andrzejewski says.
And even though Andrzejewski doesn’t want to limit the app to photo-sharing, she says that the new rating system should also lead to more picture-taking, since more ratings should encourage people to share more photos.
Other new features include the ability to hide dishes that you don’t want, and to see what your friends and Foodspotting’s expert partners (such as 7×7 magazine) recommend in any given location.
Back when Andrzejewski started the company with co-founder and CTO Ted Grubb, she says one of their visions of success was replacing the restaurant menu. Now, she says, that’s starting to come true — there have been recent meals where she looked up the restaurant on Foodspotting, saw which dishes were recommended by friends/the general Foodspotting user base, and made her choices before she’d even seen a menu.
Andrzejewski says Foodspotting will probably doing more to personalize those recommendations over time, but it sounds like it won’t be developing a super-sophisticated, Pandora-like algorithm to model your tastes. Instead, it’s going to be more basic, for example telling you that because you’ve “loved” a lot of ramen, you’ll probably like the ramen at a certain nearby restaurant.
“No one likes to feel like they’re being given a recommendation from a computer,” she says. “They want a recommendation from someone they trust.”