An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Self Organized Terminode Routing
Cluster Computing
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
Incremental collaborative filtering for mobile devices
Proceedings of the 2005 ACM symposium on Applied computing
Experimental platform for mobile information systems
Proceedings of the 11th annual international conference on Mobile computing and networking
JANE-The Java Ad Hoc Network Development Environment
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
Ad Hoc Collaborative Filtering for Mobile Networks
SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
NLWCA Node and Link Weighted Clustering Algorithm for Backbone-Assisted Mobile Ad Hoc Networks
ICN '08 Proceedings of the Seventh International Conference on Networking
MobHinter: epidemic collaborative filtering and self-organization in mobile ad-hoc networks
Proceedings of the 2008 ACM conference on Recommender systems
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Recommender systems using collaborative filtering are a well-established technique to overcome information overload in today's digital society. Currently, predominant collaborative filtering systems mostly depend on huge centralized databases to store user preferences and furthermore are only available when connected to Internet. In this paper, we consider an incremental recommender system for highly dynamic mobile environments where no central global knowledge is available and communication links are rather unreliable in comparison to static networks. We present an algorithm that aims to reach a reasonable prediction coverage and accuracy while keeping the amount of additional network overhead as small as possible, maximizing the performance of our system. For this purpose, the presented algorithm is based on a delay-tolerant broadcasting mechanism on top of a weighted cluster topology. Evaluation results show that in terms of accuracy and coverage the results of the presented algorithm converge on those obtained from a global knowledge scenario, even in the case of message loss.