• TiVo application: It is a TV show recommender system implemented in decentralized architecture. Users’ preference on shows was presented in the form of rating (from score -N to +N) and the preference score was rated in the client side. The TiVo server periodically aggregate the clients’ rating and send the rating table via ring mechanism to the client. Each client received the rating table utilize the collaborative filtering mechanism to predict the rating score of the shows he/she has never seen. Next, after the predicting process was completed, the show recommender at client side will make show recommendations for its user.
  • Fab Application: It is a web pages recommender system integrated both the content-based and collaborative filtering approaches. In Fab, there are three main system components, namely collection agents, selection agents and the central router. Collection agents are designed to collect web page of a specific topic and central router will aggregate the collected pages. Next, central router will forward pages which related to users’ interest profiles to system users and users’ selection agents will make  selection to the forwarded pages (e.g. discard pages have seen by users). In this phrase, content based mechanisms are implemented to make recommendation from central router to users. In next phrase, the concept of collaborative filtering mechanisms are applied. Selection agents at user side will forward the pages to the nearest neighbors according to some metrics for similarity calculations. To sum up, Fab combine both the well-known recommendation approaches to produce hybrid recommendations, which was running under the distributed architecture.