GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
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
MovieLens unplugged: experiences with an occasionally connected recommender system
Proceedings of the 8th international conference on Intelligent user interfaces
Studying Recommendation Algorithms by Graph Analysis
Journal of Intelligent Information Systems
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Vineyard: A Collaborative Filtering Service Platform in Distributed Environment
SAINT-W '04 Proceedings of the 2004 Symposium on Applications and the Internet-Workshops (SAINT 2004 Workshops)
PocketLens: Toward a personal recommender system
ACM Transactions on Information Systems (TOIS)
PipeCF: a scalable DHT-based collaborative filtering recommendation system
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Evaluating peer-to-peer recommender systems that exploit spontaneous affinities
Proceedings of the 2007 ACM symposium on Applied computing
Personalized and mobile digital TV applications
Multimedia Tools and Applications
TRIBLER: a social-based peer-to-peer system: Research Articles
Concurrency and Computation: Practice & Experience - Recent Advances in Peer-to-Peer Systems and Security (P2P 2006)
X-hinter: a framework for implementing social oriented recommender systems
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Collaborative filtering based on opportunistic information sharing in mobile ad-hoc networks
OTM'07 Proceedings of the 2007 OTM Confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part I
A decentralized recommendation system based on self-organizing partnerships
NETWORKING'06 Proceedings of the 5th international IFIP-TC6 conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems
A peer-to-peer recommender system based on spontaneous affinities
ACM Transactions on Internet Technology (TOIT)
Events discovery for personal video recorders
Proceedings of the seventh european conference on European interactive television conference
Vcast on facebook: bridging social and similarity networks
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Delay-tolerant collaborative filtering
Proceedings of the 7th ACM international symposium on Mobility management and wireless access
A multi-agent recommender system for supporting device adaptivity in e-Commerce
Journal of Intelligent Information Systems
Design of a P2P content recommendation system using affinity networks
Computer Communications
DocCloud: A document recommender system on cloud computing with plausible deniability
Information Sciences: an International Journal
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We focus on collaborative filtering dealing with self-organizing communities, host mobility, wireless access, and ad-hoc communications. In such a domain, knowledge representation and users profiling can be hard; remote servers can be often unreachable due to client mobility; and feedback ratings collected during random connections to other users' ad-hoc devices can be useless, because of natural differences between human beings. Our approach is based on so called Affinity Networks, and on a novel system, called MobHinter, that epidemically spreads recommendations through spontaneous similarities between users. Main results of our study are two fold: firstly, we show how to reach comparable recommendation accuracies in the mobile domain as well as in a complete knowledge scenario; secondly, we propose epidemic collaborative strategies that can reduce rapidly and realistically the cold start problem.