Seattle, WA (PRWEB) May 05, 2011
On 4/21/2011, Likester, a new Facebook “likes” aggregator, successfully predicted the bottom three contestants on American Idol™, as well as successfully predicting that Stefano Langone would be eliminated. This prediction was made by analyzing Facebook™ likes that occurred post performance (Wednesday night) and before the announcement of results (Thursday night), across hundreds of pages which are about the contestants. Today we are predicting that Jacob will be eliminated, with Lauren close behind. More details and a full week by week analysis can be found here: http://www.likester.com/pages/LikesterIdol.aspx
What is Likester?
There are several questions we’re trying to help people understand better:
What do my friends like?
However big a users social network, understanding everything their friends have liked is nearly impossible on Facebook today. Likester aggregates and organizes all this information, so one can see, which books, activities, movies, websites and more that their friends have liked.
What’s popular in my hometown? What about across the World?
Likester also aggregates the most popular things, according to the people who live in a specific location. So users can learn more about their own location, by viewing what other people who live there have liked. Or, they can learn about what people in any part of the world like. For example, what are the top movies in the United States? Or the most popular attractions in France?
What’s hot right now on Facebook?
Likester also has an up to the minute trending engine, which measures what’s hot at this very moment on Facebook. From companies, musicians, movies and more, users can understand what’s trending right now on Facebook. Users can see and take part in cultural waves as they happen, and can also see what’s trending, by location.
Finding information on something specific?
Likester also incorporates a next-generation search engine, which can search millions of objects in the Facebook universe, in a new structured and socially relevant format. For each search, users can further refine their search by category (ie, books, movies, restaurants, people). And users can also sort by popularity (show the most popular items first) or relevance (show items that have the best text match on my search term). This allows a user, for example, to search for “Seattle”, and then filter by “Bars”, and then see a list of the most popular bars in Seattle.
What else might I like?
Users of Likester can see what people who like a particular book, person, or company, also like. For example, people who like the movie “The Hangover” also like both Seth Rogan and “Get Him to the Greek”. This affinity analysis can be applied to over 50,000 different items in our databases, and users can use this data to find new things to like.
Likester is available online at http://www.Likester.com/ and Likester Idol can be played at
Contact Information: Kevin McCarthy
President and Founder