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.

MobHinter: Epidemic Collaborative Filtering and Self-Organization in Mobile Ad-Hoc Networks

SCHIFANELLA, ROSSANO;PANISSON, ANDRE';GENA, Cristina;RUFFO, Giancarlo Francesco
2008

Abstract

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.
ACM Conference On Recommender Systems
Lausanne, Switzerland
23-25 October
Proceedings of the 2008 ACM conference on Recommender systems
ACM
27
34
9781605580937
ad-hoc networks; recommender systems; social collaborative filtering
R. SCHIFANELLA; A. PANISSON; C. GENA; G. RUFFO
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/56487
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