You downloaded the same movie as two other people? There’s a good chance that you’ll also like other downloads these folks have in common. The longer a client is connected, the more files and users it is able to discover, and your recommendations get better with every download as well.From the Tribler FAQ:
Recommendations are made using a collaborative filtering algorithm.There are more details on the recommender algorithm on the Decentralized Recommendation page and in two papers, "Tribler: A social-based peer-to-peer system" (PDF) and "Distributed Collaborative Filtering for Peer-to-Peer File Sharing Systems" (PDF).
This algorithm will compare your download history to that of the peers you meet. If the peer has torrents in its download history that you have not downloaded it will recommend them to you.
The recommendation value assigned to the torrent and shown in the Recommendation window depends on how similar the peer's download history is to yours. So the higher the value, the more you are predicted to like it.
See also an old Jan 2005 Wired article by Clive Thompson where he wrote:
What exactly would a next-generation broadcaster look like? ... The network of the future will resemble Yahoo! or Amazon.com - an aggregator that finds shows, distributes them in P2P video torrents, and sells ads or subscriptions to its portal.
The real value of the so-called BitTorrent broadcaster would be in highlighting the good stuff, much as the collaborative filtering of Amazon and TiVo helps people pick good material.
Eric Garland, CEO of the P2P analysis firm BigChampagne, says, "the real work isn't acquisition. It's good, reliable filtering. We'll have more video than we'll know what to do with. A next-gen broadcaster will say, 'Look, there are 2,500 shows out there, but here are the few that you're really going to like.' We'll be willing to pay someone to hold back the tide."