Location-Based Mobile App Alike Recommends Nearby Venues That Are Just Like Your Favorites

Oh, if only I had a way of getting suggestions about where to go for coffee in an unfamiliar neighborhood. Yes, I have a tiny, portable electronic device with magic, always-on, data connectivity and apps where I can read the opinions of others about venues in my proximity. But there’s no way of really knowing if our tastes are “Alike” and so I’m stuck trusting the recommendations of strangers, who might have really horrible taste in coffee. Maybe they like Dunkin’ Donuts, and I don’t like Dunkin’ Donuts.

What I really want is an app that will tell me a place nearby that will point me to venues aligned with my own tastes and interests. An app like Alike. The recently released Alike iPhone app is designed to let you know about nearby restaurants, bars, and cafes that are like those you’re already a fan of. It’s pretty simple to use: You just open it up, type in the name of one of your favorite spots, and it’ll suggest others like it in your proximity.

The app ranks recommendations based on how “alike” they are to your search queries, allows users to see how far away a place is, and lets them provide feedback about whether they think one place is like another. Venue pages provide more detail, including address, phone number, hours, etc., and lets users bookmark them for later.

There’s also all the necessary social hooks: Users can connect their Facebook and Twitter accounts to share with other users. And if they connect Foursquare, they can see how many users have gone to a certain venue, and the app even provides thumbnails for friends who have checked in there.

The basic idea behind Alike is to point users to nearby locations that are similar to those they’re already fans of. You know, “if you like this, then you’ll probably like that.” That’s probably not altogether useful in your own neighborhood — you probably already know that if you like Ritual Coffee Roasters, you should check out Four Barrel down the street, or vice versa. But if you’ve never been to Portland and are visiting, Alike will probably suggest you check out Stumptown Coffee, based on your already-registered affinities.

It’s a pretty straightforward recommendation system, but the real work is being done on the back end. Founder Maria Zhang is a big data and machine learning enthusiast who previously worked at Microsoft on the Personal Relevance team at MSN. And as a result, Alike is more of a big data platform than a pure local recommendations app. The real goal here is to collect data based on user behavior and response, and help the Alike engine improve its recommendations and the suggestions it makes as time goes on.

On the business side, Alike makes money through affiliate partnerships with third-party providers that aggregate deals for local venues. Zhang doesn’t seem to be interested in doing sales of these types of deals within Alike itself, and that’s probably for the better, since that might color the results. For now, the team is perfectly content with just being a pure tech play that someone else can sell against, even if it means at venues that aren’t highly ranked in a person’s interest or taste graph.