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Ness Launches Personal Search Engine, Starting With Restaurants
Aug 25, 2011 (06:08 PM EDT)
Ness Computing Aims to Lead the Next Generation of Search: Results Tailored to a Person's Tastes
SILICON VALLEY, Calif., Aug. 25, 2011 /PRNewswire/ -- Ness Computing today announced that its personal search engine app for iPhone and iPod touch is now available on the App Store as a Featured App. The app, called Ness, changes the search landscape in three ways: (1) results are tailored to each individual person's preferences rather than one-size-fits-all, (2) content is from trusted friends rather than strangers, and (3) the app intelligently adapts to a person's unique tastes over time.
The first category Ness supports is restaurants.
Instead of providing a long list of reviews to sift through, Ness recommends restaurants based on the learned likes and dislikes of each individual, as well as his or her friends. The result is search that is tailored to each person, for more relevant recommendations and quicker decisions on the go.
Compared to existing search engines, Ness is more.
Existing search and recommendation services simply aggregate reviews from strangers without regard to the tastes and preferences of the person doing the search. Under the current search paradigm, ten different people looking for Italian restaurants in San Francisco all receive the same set of results.
Ness moves far beyond this, and in doing so, is helping lead the next generation of search: a personally relevant experience driven by mobile and social data.
"Existing search engines don't have a sense of who you are; they just know what's available," said Corey Reese, CEO and Co-Founder of Ness Computing. "Ness is a better way to discover new restaurants on the go because its results are specifically tailored to you. The future of search lies in anticipating the wants of the consumer and delivering intelligent, personally relevant results."
Ness makes a leap forward in technology.
Ness is driven by the company's internally developed Likeness Engine, which uses advanced techniques in machine learning (including collaborative filtering), social graph data mining, and natural language processing. To make recommendations, Ness weighs information from many different sources, including a person's taste profile, his or her similarity to other users, the total popularity of each restaurant, and trusted recommendations from friends on third-party services like Facebook and Foursquare. Ness then computes a Likeness Score of 0-100% that predicts how much the person will enjoy each recommended restaurant.
Since different people decide where to eat using different criteria (an intricate balance of personal taste, recommendations from friends, location, ambience, and other factors), each person's results are unique to them. The more a person uses Ness, the more personalized it is to their tastes.
Ness embraces a detail driven design philosophy and explores the full visual capabilities of iOS with a beautiful interface designed to help people quickly decide where to eat on the go.
Ness is designed for people who love food.
In future releases, Ness will extend its offering to other lifestyle categories, including music, shopping, nightlife, and entertainment, all within the same app.
About Ness Computing
Ness Computing's mission is to make search personal. By applying its expertise in search, recommendations and social networking to human behavior, Ness helps people discover experiences they'll love.
Ness Computing has assembled a world-class team with backgrounds in information retrieval, applied machine learning, natural language processing, collaborative filtering, and user interface engineering. Its team members have built successful products and technologies at Apple, Google, Ning, Oracle, Palantir and Yahoo!. The company is venture-backed by Khosla Ventures, Alsop Louie Partners, TomorrowVentures, Bullpen Capital, and a co-founder of Palantir. Advisors include the creators of Farmville and Mint. The company is based in Los Altos, California and is hiring. For more information, please visit http://www.likeness.com.
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