A scalable P2P recommender system based on distributed collaborative filtering

Han, Peng; Xie, Bin; Yang, Fan; Shen, Ruimin

doi:10.1016/j.eswa.2004.01.003

Cited by 107 publications

(60 citation statements)

References 8 publications

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“…[74][43] [41]). Manouselis and Costopoulou [61] combined all these evaluation criteria in a comprehensive classification framework with three main categories: 1.…”

Section: Classification Framework For Tel Recsys Reviewmentioning

confidence: 99%

Panorama of Recommender Systems to Support Learning

Drachsler

Verbert

Santos

et al. 2015

Recommender Systems Handbook

192

115

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“…[74][43] [41]). Manouselis and Costopoulou [61] combined all these evaluation criteria in a comprehensive classification framework with three main categories: 1.…”

Section: Classification Framework For Tel Recsys Reviewmentioning

confidence: 99%

Panorama of Recommender Systems to Support Learning

Drachsler

Verbert

Santos

et al. 2015

Recommender Systems Handbook

192

115

View full text Add to dashboard Cite

“…Several efforts have recently concentrated on decentralised recommenders [14,24,2,6,27] to investigate their advantages in terms of scalability and privacy. Earlier approaches exploit DHTs in the context of recommendation.…”

Section: Related Workmentioning

confidence: 99%

“…Earlier approaches exploit DHTs in the context of recommendation. For example, PipeCF [14] and PocketLens [24] propose Chord-based CF systems to decentralise the recommendation process on a P2P infrastructure. Yet, more recent solutions have focused on using randomised and gossip-based protocols [5,18,4].…”

Section: Related Workmentioning

confidence: 99%

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

Frey

Kermarrec

Maddock

et al. 2015

Distributed Applications and Interoperable Systems

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Abstract. Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. In this paper, we propose a framework to develop dynamically adaptive decentralised recommendation systems. Our proposal supports a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system's mission.

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“…It is of course possible to apply distributed hash tables [17]. Here, users are stored in a hash table and they are indexed by (item, rate) pairs as keys.…”

Section: Related Workmentioning

confidence: 99%

Overlay Management for Fully Distributed User-Based Collaborative Filtering

Ormándi

Hegedűs

Jelasity

2010

Euro-Par 2010 - Parallel Processing

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Abstract. 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.

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A scalable P2P recommender system based on distributed collaborative filtering

Cited by 107 publications

References 8 publications

Panorama of Recommender Systems to Support Learning

Panorama of Recommender Systems to Support Learning

Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders

Overlay Management for Fully Distributed User-Based Collaborative Filtering

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