A new food app that claims to tailor restaurant recommendations to individual tastes may be paving the way for the next generation of search tools.
While existing search and recommendation services simply aggregate reviews from strangers around the web, the makers of Ness say their app delves deeper by delivering restaurant search results based on the user’s personal tastes and preferences.
“Existing search engines don’t have a sense of who you are. They just know what’s available,” said Ness CEO Corey Reese. “…The future of search lies in anticipating the wants of the consumer and delivering intelligent, personally relevant results.”
Ness works once the user rates 10 restaurants they’ve visited. The more often the user provides a rating, the more Ness learns about the diner and returns personalized results that reflect his or her unique tastes.
To make recommendations, the app — driven by mobile and social data — weighs information from different sources that include the person’s taste profile; their similarity to other users; the popularity of an eatery; and recommendations from friends on Facebook or Foursquare.
Based on the data, the app then computes a Likeness score of 0 to 100 percent that predicts how much the person will enjoy the restaurant.