Tired of digging through long-winded restaurant reviews to find a great meal? Next time, turn to your smartphone where personalized search engines will lead your stomach in the right direction.
A newcomer in the world of personalized search, Ness Computing recently released a free iOS app that provides restaurant recommendations based on a user’s personal tastes and information from friends gathered through social media sources such as Foursquare and Facebook.
The app uses that information to calculate a “Likeness Score,” which is the probability a user will enjoy a particular venue. Integration with social networks is optional; however, it is one way Ness can achieve more relevant results.
“There’s all sorts of information that people’s friends have left when they check in [to a venue], or mention: ‘I’m having a great meal at this place,’” Ness Computing co-founder and CEO Corey Reese said. “We wanted to build a beautiful interface for people to find that kind of content and information on their mobile device.”
The app, which some bloggers compare to Netflix (movies and TV) or Pandora (music) for restaurants, provides information such as addresses, phone numbers, comments and tips from social networks.
Reese said user design has been a core focus for Ness. Looking to Apple as a model, the firm hired Scott Goodson as director of mobile engineering. Goodson was a former engineer at Apple who was one of the first members of the iOS team and helped build some of its original flagship apps.
“When you talk to the folks at Apple on their design and engineering teams, it’s almost a religious experience to make this stuff. And we think part of the reason they’ve been so successful is because they take their product development so seriously,” Reese said.
The underlying technology took 18 months to develop with a lot of effort put into refining an index of restaurants and standardizing data from different sources.
“When we ran one of our first experiments, McDonald’s was obviously one of the most popular restaurants to show up in our system. There were 1,900 ways that our data sources had spelled McDonalds,” Reese said. “We had to clean all of that data so that there was only one way of spelling McDonald’s and then associate all that data with each other.”
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