BARBOSA, L. N.GEMMELL, J. F.HORVATH, M.HEIMFARTH, T.2022-01-122022-01-122020-08-01BARBOSA, L. N.; GEMMELL, J. F.; HORVATH, M.; HEIMFARTH, T. Assessing distributed collaborative recommendations in different opportunistic network scenarios. International Journal of Grid and Utility Computing, v. 11, n. 5, p. 646-661 aug. 2020.1741-8488https://repositorio.fei.edu.br/handle/FEI/3668© 2020 Inderscience Enterprises Ltd.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.Acesso RestritoArtigo10.1504/IJGUC.2020.110052Decentralised recommender systemsDevice-to-device communicationsMachine learningMobile ad hoc networksOpportunistic networksRecommender systemsUser-based collaborative filtering