With Its Biggest Update Yet, Ness Debuts Instant, Personalized Restaurant Recommendations, Says Events, Nightlife Are Next

Ness, the restaurant recommendations app that uses social data combined with machine-learning techniques to offer personalized suggestions, has released the next major version of its application today, now focused on what it’s calling “instant recommendations.” In the earlier version, Ness relied on user-initiated searches and a setup wizard that asked users to select their favorite cuisines and rate restaurants. Today, users are instead taken directly to the app’s homescreen for immediate recommendations based on time of day, location and popularity. When new users first launch the revamped app, they’re shown only a couple of explanatory screens before seeing the list of popular places nearby. Going forward, training the app to learn your tastes by rating venues or liking cuisines is now an optional feature, accessible through the “Personalize” setting. While this onboarding process obviously makes Ness easier for newcomers testing the app out for the first time, it also allows Ness’ previous users a more “lean-back” type of experience, as well. Now they don’t have to launch the app then kick off searches; Ness will just know what they like and make suggestions. Ness co-founder and CEO Corey Reese tells us that even if users never dive into the “Personalize” section, the app is now able to improve its recommendations in time. It also lets users rate places from restaurants’ cards themselves, slowly building up a database of user likes and dislikes. However, Ness can implicitly learn a user’s tastes, too, says?Reese. “Let’s say you don’t tap on Vietnamese places ever – Ness will pick up on that. It will start showing you fewer Vietnamese places,” Reese explains. “But let’s say that every time you see a Japanese restaurant, you tap on that, and take a look at dinner time, it will start showing you more Japanese restaurants at dinner time.” It’s a simple enough concept, but it has taken the company two years of building to get to this point. The company has spent a large part of that time combining the data it licenses from various vendors and matching it up with data normalized across social services, including Facebook, Foursquare, Instagram and OpenTable. Ness also now uses social data, among other things, to explain why it made the recommendations it did. For example, it might tell you that several of your friends have tried this sushi place. Or it might be more of a matter of two places

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