Title: Assessing distributed collaborative recommendations in different opportunistic network scenarios
Authors: Lucas Nunes Barbosa; Jonathan F. Gemmell; Miller Horvath; Tales Heimfarth
Addresses: Computer Science Department, Universidade Federal de Lavras (UFLA), Lavras, Minas Gerais, Brazil ' College of Computing and Digital Media, DePaul University, Chicago, Illinois, USA ' Computer Science Department, Centro Universitário FEI, São Bernardo do Campo, São Paulo, Brazil ' Computer Science Department, Universidade Federal de Lavras (UFLA), Lavras, Minas Gerais, Brazil
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.
Keywords: opportunistic networks; recommender systems; mobile ad hoc networks; decentralised recommender systems; user-based collaborative filtering; device-to-device communications; machine learning.
International Journal of Grid and Utility Computing, 2020 Vol.11 No.5, pp.646 - 661
Received: 18 Sep 2018
Accepted: 15 Feb 2019
Published online: 02 Oct 2020 *