ACM Transactions on Computer Systems (TOCS)
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
The little engine(s) that could: scaling online social networks
Proceedings of the ACM SIGCOMM 2010 conference
Exploiting place features in link prediction on location-based social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
The GOSSPLE anonymous social network
Proceedings of the ACM/IFIP/USENIX 11th International Conference on Middleware
Epidemic-Style management of semantic overlays for content-based searching
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
T-Man: gossip-based overlay topology management
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
Geology: Modular Georecommendation in Gossip-Based Social Networks
ICDCS '12 Proceedings of the 2012 IEEE 32nd International Conference on Distributed Computing Systems
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Decentralised social networks promise to deliver highly personalised, privacy-preserving, scalable and robust implementations of key social network features, such as search, query extensions, and recommendations. Such systems go beyond traditional online social networks by leveraging implicit social ties to implement personalised services. Yet, current decentralised social systems usually treat all users uniformly, when different sub-communities of users might in fact work best with different mechanisms. In this paper, we look at the specific case of decentralised social networks seeking to cluster users exhibiting similar behaviours to provide decentralised recommendations. These decentralised recommendation systems typically rely on a single metric applied uniformly to all users to extract similarities, while it seems natural that there is no such one-size-fits-all approach. More specifically we show in this paper, using a real Twitter trace, that (i) individual users can benefit from a personalised strategy in the context of decentralised recommendation systems, and that (ii) overall system performance is improved when the system accounts for the varying needs of its users i.e. when each user is allowed to diverge and use its optimal strategy.