A scalable location service for geographic ad hoc routing
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Peer-to-peer based recommendations for mobile commerce
WMC '01 Proceedings of the 1st international workshop on Mobile commerce
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
A decentralized CF approach based on cooperative agents
Proceedings of the 15th international conference on World Wide Web
Distributed collaborative filtering for peer-to-peer file sharing systems
Proceedings of the 2006 ACM symposium on Applied computing
TopGen - internet router-level topology generation based on technology constraints
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
The HyperVerse: concepts for a federated and Torrent-based '3D Web'
International Journal of Advanced Media and Communication
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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.