Collaborative filtering via epidemic aggregation in distributed virtual environments

The ever-increasing amount of available information in today's digital society necessitates inline techniques for determining the most relevant content. Collaborative filtering (CF) systems have proven to be an adequate means for reducing informational overload and generating useful recommendations. Current systems are predominantly built on centralized or, more recently, structured Peer-to-Peer (P2P) approaches. However, in order to apply collaborative filtering to large-scale distributed virtual environments (DVEs) in unstructured networks with substatially higher user numbers, different approaches are necessary. Within this paper we present a collaborative filtering algorithm for DVEs utilizing epidemic data aggregation based exclusively on local information. Designed to be extremely scalable, it creates recommendations in a transparent way by distributing an accumulated view of favorite ratings to interacting users. The algorithm is intended for deployment in the HyperVerse - a self-organizing middleware service for large-scale DVEs - for generating and managing rating predictions of object favorites. Evaluation results show that, in terms of quality, locally aggregated predictions converge well on those obtained from an idealized global view.