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Get smart, use cell phone to find your restaurant
Source: Agencies |
September 6, 2011, Tuesday
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Print Edition
Print Edition
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'," said Corey Reese, co-founder and CEO of Ness Computing. "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 and phone numbers, check-ins, comments and tips from social networks. Check-ins amongst friends influence a restaurant's ranking in search results.
The app provides the ability to filter out major chains, which appeal to people looking for independent restaurants. And to facilitate finding new places to eat, users can hide restaurants they have already rated. Search results can also be filtered by distance and price.
"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," said Reese. "We had to clean all of that so that there was only one way of spelling McDonald's and then associate all that data with each other."
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'," said Corey Reese, co-founder and CEO of Ness Computing. "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 and phone numbers, check-ins, comments and tips from social networks. Check-ins amongst friends influence a restaurant's ranking in search results.
The app provides the ability to filter out major chains, which appeal to people looking for independent restaurants. And to facilitate finding new places to eat, users can hide restaurants they have already rated. Search results can also be filtered by distance and price.
"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," said Reese. "We had to clean all of that so that there was only one way of spelling McDonald's and then associate all that data with each other."
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