Today Nara launched mobile versions of its personalized restaurants recommendation tool, available for free for both iOS and Android. Along with the web platform, Nara’s mobile apps help users find restaurant recommendations in cities in the U.S. and Canada, including New York City, San Francisco and Toronto. The Nara platform launched in beta in June 2012 in eight cities, and today marks the first mobile apps, and an expansion to 25 cities.
Unlike restaurant recommendation tools like Urbanspoon, which recommend restaurants based on what’s hot in a given city, or based on proximity and price, Nara takes a personalized approach to its recommendations, attempting to understand users’ preferences and making personalized recommendations based on a user’s “digital DNA.”
Founder and CEO Tom Copeman founded Cambridge, MA-based Nara Logics in 2010 to work on the Nara Neural Network, a web recommendation engine. He worked with MIT PhD Dr. Nathan Wilson, now Nara’s CTO, on the technology, which he says is “neuroscience combined with computer science,” analyzing datasets based on how the brain thinks. Copeman said he was inspired to start Nara because he found it difficult to find relevant things to do while traveling, and the app is aimed at both travelers and locals.
“I envisioned a platform that actually works for me on my own behalf out on the web, that really got to know me and understand me and know what my likes and dislikes and preferences were,” Copeman said in an interview. “It’s really about turning searching into finding. We strongly believe that the way that the web has evolved over the last 3-5 years, search has become less and less relevant to us, yet it’s taking up so much more of our time.”
Users can sign up via the mobile apps or on the website by completing a quiz about their dining preferences, from the types of restaurants they prefer, to their favorite cuisine, to two restaurants they like in one of Nara’s supported cities. After recommendations are displayed, users can give them a thumbs up or a thumbs down to help the system understand their preferences, and they can filter recommendations by price, neighborhood, and cuisine type. Users can “pin” restaurants for later, akin to building an Urbanspoon wishlist, and make a reservation via an OpenTable integration.
Nara raised $4 million in Series A funding in June, and Copeman said they raised funding primarily to expand and grow the team, and the focus for the next few months is to extend the categories in the app, and to continue to hire. The company plans to expand beyond restaurants, becoming a recommendation engine for anything, primarily consumer lifestyle categories. “The platform itself is really scalable, and restaurants is just the start,” he said. In terms of monetization, Copeman said the algorithm isn’t motivated by sponsored search or targeted ads, and they plan to monetize through affiliate booking partners, and via other channels he didn’t specify.
Nara is just one of several companies trying to tap into a user’s interest graph to make personalized recommendations. Personal assistant apps like Saga and Cue are already looking at a user’s day to make recommendations on everything from restaurants to activities, and social recommendations apps like Jybe are trying to do what Nara is doing for books and movies, as well as restaurants. Not to mention Ness, which raised $15 million in August for its personalized recommendations technology, and started by tackling restaurant recommendations.
Copeman said he’s trying to set Nara apart by focusing on making it useful right away, not requiring any recommendations from friends or other Nara users to get started. “Nara works for the first user the first time, there’s no cold start problem. We don’t rely on the social layer, you don’t need to bring friends on into Nara…and you don’t need other Nara users in and of themselves to start getting deep web personalized recommendations.”
With companies like Hunch, which was acquired by eBay, and more recently Xen, trying to map a user’s interest graph across restaurants, movies, books, activities, places, and everything else, Nara has its work cut out for it if it wants to be the default choice for recommendations. Moving into other lifestyle categories will be key to the company’s success, as will partnering with other companies like OpenTable to let people not only find restaurants and activities, but book them directly from the app.
