Assessing distributed collaborative recommendations in different opportunistic network scenarios. (1st August 2020)

• Record Type: Journal Article
• Authors: Barbosa, Lucas Nunes; Gemmell, Jonathan F.; Horvath, Miller; Heimfarth, Tales
• Is Part Of: International journal of grid and utility computing. Volume 11:Number 5(2020)
• Page Start: 646
• Page End: 661
• Publication Date: 2020-08-01
• Subjects: opportunistic networks -- recommender systems -- mobile ad hoc networks -- decentralised recommender systems -- user-based collaborative filtering -- device-to-device communications -- machine learning; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

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.