Nara: App Gets Smarter So You Eat Better (Even in a New City)

Is Nara, the new restaurant suggestions app (also accessible online), the food-obsessed love child of Pandora and Yelp? With its ability to narrow recommendations based on your feedback plus its large database of reviews, it’s certainly got that potential. The “advanced restaurant recommendation engine” was built by MIT scientists and, like Pandora, the more you rate, the smarter it gets.

Already Good

It’s an app geared toward “finding, not searching,” a particularly key distinction for travelers who are already in a place and don’t want to spend a huge amount of time getting to know the lay of the land before finding a restaurant.

My favorite thing about it is that you can use a city you know (if you live in or near one of the 25 cities currently covered) to build your store of ratings, which will help the system choose the right restaurants for you in cities you aren’t as familiar with.

The layout is pretty and easy to use. You get pictures and key bits of information on one scrollable screen. And with OpenTable reservations built right in, you don’t have to move between apps to choose and book restaurants. You can also pin restaurants to save them for later (a great way to keep track of places you want to try) and make notes to yourself about individual spots.

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Room for Improvement

When I took it for a spin, the major downside I noticed was that results are currently limited to 25 cities in the U.S. and Canada. This seems like it would be a particularly powerful timesaving tool in countries where I didn’t speak the language but still wanted to have a great meal. Another issue I came across a few times was that some restaurants’ location tags were off, sometimes by an entire city.

Finally—and this is a personal one more than an issue with the app—I sometimes had a hard time distilling my opinion of a restaurant into the thumbs-up/thumbs-down dichotomy. I found myself wanting the option to break down my opinion in a more nuanced way (I liked the food but hated the service) so that the system could better learn my preferences.

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