Likester Launches New Recommendation Engine, After Having Tracking Over a Half-Billion Facebook “Likes"

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Likester™ was introduced less than six months ago and has since tracked over 500 million likes for its users. Applying the predictive statistical power such large numbers make possible, the company today released Likester Affinities, a broad-ranging recommendation engine using advanced proprietary statistical methods.

“The accuracy of Affinities’ recommendations can even be a bit unnerving at first,” said Kevin McCarthy, CEO of Likester Corporation.

Likester™ was introduced less than six months ago and has since tracked over 500 million likes for its users. Applying the predictive statistical power such large numbers make possible, the company today released Likester Affinities, a broad-ranging recommendation engine using advanced proprietary statistical methods.

For people who enjoy browsing recommendations on movie sites, perusing suggestions on book sites, or for anyone who just doesn’t want to miss out on good things, Likester Affinities provides the ultimate recommendation experience. With greater precision and broader scope than other alternatives, Likester Affinities recommends not only books or movies, but draws from hundreds of different categories.

An affinity is what a person feels when they identify with something. Using proprietary algorithms, Likester Affinities presents its users with a large number of personally-tailored recommendations: TV shows, local businesses, nightlife, musicians, vacations, restaurants, books, movies and relevant suggestions from dozens of other categories: all the items that they are most likely to identify with, their personal affinities.
Likester Affinities’ recommendations are developed based on four dimensions:
a) who the user is (theirFacebook profile)
b) what people who are similar to themselves have liked (the Facebook likes of people who share your the users demographics)
c) what the user has "liked" (on Facebook)
d) what the people who share the users Facebook likes have additionally liked (on Facebook).

Using this information, and drawing from the hundreds of millions of Facebook likes tracked by Likester, a users Likester Affinities’ recommendations should prove very appealing and engaging.

“The accuracy of Affinities’ recommendations can even be a bit unnerving at first,” said Kevin McCarthy, CEO of The Likester Corporation. “With over a half billion likes to work with, the algorithms behind Likester Affinities produce astonishingly good recommendations. I am myself sometimes amazed by how well Likester seems to know me, and by how uncanny Affinities’ suggestions are.”

In addition to producing recommendations of new things in hundreds of categories that people may enjoy, Likester Affinities offers games to play, testing knowledge of the users friends’ interests. It also provides ways for you to explore both the users, and their friends’ likes.

Moreover, users can actively manage their likes, which are now categorized for easy access. For the first time, people can curate the things they have liked, to ensure that their desired level and type of self-expression is happening on their Facebook profile. Users are also able to view (and freely offer) shrewd recommendations for their friends’ consideration, by using Affinities’ analysis of their profiles and likes. Users can learn more about what particular friends might like, or simply enjoy the fun of sending their friends cool Affinities’ recommendations directly from Likester.

Likester Affinities is now available at http://www.likester.com or http://apps.facebook.com/likester/

To schedule an interview, contact Kevin McCarthy at 206-217-1831, or Kevin(at)Likester(dot)com

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Kevin McCarthy
Likester
(206) 217-1831
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