Self-organizing collaborative filtering in global-scale massive multi-user virtual environments
Alexander Höhfeld, Patrick Gratz, Angelo Beck, Jean Botev, Hermann Schloss, Ingo Scholtes · 2009
Due to the huge amount of available information in today's society, it becomes more and more difficult for the consumer to locate the most useful information for a specific topic. Recommender systems using collaborative filtering (CF) are a popular technique for reducing information overload and finding useful information on the Internet. However, in massive global-scale multi-user virtual environments different approaches are required from those used within the currently dominant centralized infrastructures or lately investigated P2P approaches. Within this paper we present a novel collaborative filtering algorithm used within the HyperVerse -- a P2P-based self-organizing middleware service for massively distributed virtual worlds -- to generate and manage recommendations for HyperVerse object favorites. Due to its global extent considering users and possible ratings, using a monolithic database-backed recommendation service or huge profile- or item-rating-matrices does not scale in our scenario. The decentralized approach presented within this paper creates per user ratings in an adaptive and transparent way by comparing public favorites of passer-by users with personal peer data, weighted by self-adjusting buddy lists.