Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/56068
Title: Distributed recommender systems on an opportunistic network environment
Other Titles: Sistemas de recomendação distribuídos em um ambiente de rede oportunista
Authors: Heimfarth, Tales
Gemmell, Jonathan
Heimfarth, Tales
Gemmell, Jonathan
Giacomin, João Carlos
Freitas, Edison Pignaton de
Keywords: Opportunistic networks
Recommender systems
Mobile ad hoc networks
Redes oportunistas
Sistema de recomendação
Redes móveis ad hoc
Issue Date: 28-Feb-2023
Publisher: Universidade Federal de Lavras
Citation: BARBOSA, L. N. Distributed recommender systems on an opportunistic network environment. 2023. 59 p. Dissertação (Mestrado em Ciência da Computação)–Universidade Federal de Lavras, Lavras, 2020.
Abstract: Mobile devices are common throughout the world, even in counties with limited internet access and even when natural disasters disrupt access to a centralized 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 alleviate this burden. The project presents a novel collaborative filtering recommender system based on an opportunistic distributed network. Collaborative filtering algorithms are widely used in many online systems. Often, the computation of these recommender systems is performed on a central server, controlled by the provider, requiring constant internet connection for gathering and computing data. However, in many scenarios, such constraints cannot be guaranteed or may not even be desired. On the proposed recommendation engine, users share information via an opportunistic network independent of a dedicated internet connection. Each node is responsible for gathering information from nearby nodes and calculating its own recommendations. Using a centralized collaborative filtering recommender as a baseline, we evaluate three simulated scenarios composed by different movement speeds and data exchange parameters. Our results show that in a relatively short time, an opportunistic distributed recommender systems can achieve results similar to a traditional centralized system. Furthermore, we noticed that the speed at which the opportunistic recommender system stabilizes depends on several factors including density of the users, movement speed and patterns of the users, and transmission strategies. On future works we will analyze new strategies and datasets, likewise, we will increase the number of users on different scenarios.
URI: http://repositorio.ufla.br/jspui/handle/1/56068
Appears in Collections:Ciência da Computação - Mestrado (Dissertações)

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