Going on the theory that 500 million Facebook likes can’t be wrong, Likester debuted a recommendation search engine.
Likester Affinities uses proprietary statistical methods to help recommend TV shows, local businesses, nightlife, musicians, vacations, restaurants, books, movies, and more than 100 other categories, based on:
- Who you are (users’ Facebook profiles);
- What people similar to you have liked (likes of users with similar demographics);
- What you like (users’ Facebook likes); and
- What people who share users’ Facebook likes have liked.
Likester Affinities also offers users games that test their knowledge of their friends’ interests, and the app allows users to explore their own likes and their friends’ likes.
Likes can be managed and curated, as well as shared with Facebook friends.
Likester Chief Executive Officer Kevin McCarthy said:
The accuracy of Affinities’ recommendations can even be a bit unnerving at first. 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.
Readers: Do you think an analysis of your Facebook likes and those of your friends can produce solid recommendations?