A new search start-up, Ness, is aiming to provide much more personalized search results than what exists on sites from Google to Yelp.
The mobile app, launching Thursday, recommends restaurants based on the interests of people with similar tastes. It also includes search results from friends, which can be included via Facebook and Foursquare.
To use Ness, people download the app and rate ten nearby restaurants. Then they can see a list of restaurants nearby or in a particular neighborhood ordered by Ness’ personalized algorithm. Or people can click to select different cuisines such as sushi or Mexican food. After hitting the search button, a list of restaurants will appear with a “Likeness Score” for each restaurant, from 1% to 100%. This number is a personalized match for each user based on his or her personal preferences, the preferences of his or her friends, and the restaurant’s overall popularity. The company is thus named Ness, which is short for each individual’s “likeness.” As people rate each restaurant, the service will improve its recommendations.
The problem that Ness is trying to solve is a big one that goes beyond restaurants into a number of other verticals. What if there were a way to find only the hotels on TripAdvisor that are relevant to my own tastes? A number of start-ups provide travel results based on friend recommendations but do not provide the algorithm or personalized filtering. Because web 2.0 companies have enabled such as massive amount of user-generated content, it has become more difficult to find personally-relevant businesses, media or other content.