Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/46705
metadata.artigo.dc.title: Assessing distributed collaborative recommendations in different opportunistic network scenarios
metadata.artigo.dc.creator: Barbosa, Lucas Nunes
Gemmell, Jonathan F.
Horvath, Miller
Heimfarth, Tales
metadata.artigo.dc.subject: Opportunistic networks
Recommender systems
Mobile ad hoc networks
Decentralised recommender systems
User-based collaborative filtering
Device-to-device communications
Machine learning
Redes oportunistas
Sistemas de recomendação
Redes ad hoc móveis
Filtragem colaborativa baseada no usuário
Comunicações dispositivo a dispositivo
Aprendizado de máquina
metadata.artigo.dc.publisher: Inderscience Publishers
metadata.artigo.dc.date.issued: 2020
metadata.artigo.dc.identifier.citation: BARBOSA, L. N. et al. Assessing distributed collaborative recommendations in different opportunistic network scenarios. International Journal of Grid and Utility Computing, [S. l.], v. 11, n. 5, 2020. DOI: 10.1504/IJGUC.2020.110046.
metadata.artigo.dc.description.abstract: Mobile devices are common throughout the world, even in countries with limited internet access and even when natural disasters disrupt access to a centralised infrastructure. This access allows for the exchange of information at an incredible pace and across vast distances. However, this wealth of information can frustrate users as they become inundated with irrelevant or unwanted data. Recommender systems help to alleviate this burden. In this work, we propose a recommender system where users share information via an opportunistic network. Each device is responsible for gathering information from nearby users and computing its own recommendations. An exhaustive empirical evaluation was conducted on two different data sets. Scenarios with different node densities, velocities and data exchange parameters were simulated. Our results show that in a relatively short time when a sufficient number of users are present, an opportunistic distributed recommender system achieves results comparable to that of a centralised architecture.
metadata.artigo.dc.identifier.uri: https://doi.org/10.1504/IJGUC.2020.110046
http://repositorio.ufla.br/jspui/handle/1/46705
metadata.artigo.dc.language: en_US
Appears in Collections:DCC - Artigos publicados em periódicos

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