GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Peer-to-peer based recommendations for mobile commerce
WMC '01 Proceedings of the 1st international workshop on Mobile commerce
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
A Two-Level Semantic Caching Scheme for Super-Peer Networks
WCW '05 Proceedings of the 10th International Workshop on Web Content Caching and Distribution
Emerging semantic communities in peer web search
P2PIR '06 Proceedings of the international workshop on Information retrieval in peer-to-peer networks
A Trust-enabled P2P Recommender System
WETICE '06 Proceedings of the 15th IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises
ACM Transactions on Computer Systems (TOCS)
P2P '07 Proceedings of the Seventh IEEE International Conference on Peer-to-Peer Computing
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Modeling Preferences in a Distributed Recommender System
UM '07 Proceedings of the 11th international conference on User Modeling
The long tail of recommender systems and how to leverage it
Proceedings of the 2008 ACM conference on Recommender systems
Non-linear matrix factorization with Gaussian processes
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
T-Man: Gossip-based fast overlay topology construction
Computer Networks: The International Journal of Computer and Telecommunications Networking
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Collaborative filtering using random neighbours in peer-to-peer networks
Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
Challenges in Personalizing and Decentralizing the Web: An Overview of GOSSPLE
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
Epidemic-Style management of semantic overlays for content-based searching
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Asynchronous peer-to-peer data mining with stochastic gradient descent
Euro-Par'11 Proceedings of the 17th international conference on Parallel processing - Volume Part I
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Offering personalized recommendation as a service in fully distributed applications such as file-sharing, distributed search, social networking, P2P television, etc, is an increasingly important problem. In such networked environments recommender algorithms should meet the same performance and reliability requirements as in centralized services. To achieve this is a challenge because a large amount of distributed data needs to be managed, and at the same time additional constraints need to be taken into account such as balancing resource usage over the network. In this paper we focus on a common component of many fully distributed recommender systems, namely the overlay network. We point out that the overlay topologies that are typically defined by node similarity have highly unbalanced degree distributions in a wide range of available benchmark datasets: a fact that has important--but so far largely overlooked--consequences on the load balancing of overlay protocols. We propose algorithms with a favorable convergence speed and prediction accuracy that also take load balancing into account. We perform extensive simulation experiments with the proposed algorithms, and compare them with known algorithms from related work on wellknown benchmark datasets.