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dc.creatorLasmar, Eduardo L.-
dc.creatorPaula, Fábio O. de-
dc.creatorRosa, Renata L.-
dc.creatorAbrahão, Julia I.-
dc.creatorRodríguez, Demóstenes Z.-
dc.date.accessioned2020-05-13T14:43:34Z-
dc.date.available2020-05-13T14:43:34Z-
dc.date.issued2019-12-
dc.identifier.citationLASMAR, E. L. et al. RsRS: Ridesharing recommendation system based on social networks to improve the user’s QoE. IEEE Transactions on Intelligent Transportation Systems, [S.l.], v. 20, n. 12, p. 4728-4740, Dec. 2019.pt_BR
dc.identifier.urihttps://ieeexplore.ieee.org/document/8865572pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/40870-
dc.description.abstractNowadays, one of the most outstanding new urban transport model is the ridesharing service, in which two or more users share a ride. This transport model reduces costs and the number of circulating vehicles, improving user mobility. In the ridesharing service, the users' quality is a tangible novel evaluation parameter. Consequently, this study treats the use of quality of experience (QoE) in the ridesharing service context, proposing a recommendation system (RS) for ridesharing services (RsRS), which considers user profile information extracted from online social networks (OSN) and user preferences. Thus, the main objective of the proposed RsRS is to improve users' QoE. To this end, the users' profile for the ridesharing service is built based on OSN data, which includes group of users with similar characteristics in the same trip, thus avoiding users with opposite preferences. First, subjective tests are carried out to obtain information on users' preferences and the results are analyzed via machine learning algorithms to determine the various user profiles. The experimental results demonstrate that the random forest algorithm has the best performance, considering OSN data and explicit preferences saved in the proposed solution and only OSN data, for average F-measures of 0.92 and 0.91, respectively. Additionally, a ranking containing a list of recommended users to share a ride is determined using a similarity function, and the results demonstrate that 94.2% of assessors agree with the proposed recommendations. Furthermore, the RsRS has a modular configuration and its integration with a real ridesharing service providers is also discussed.pt_BR
dc.languageen_USpt_BR
dc.publisherInstitute of Electrical and Electronic Engineerspt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceIEEE Transactions on Intelligent Transportation Systemspt_BR
dc.subjectMachine learning algorithmspt_BR
dc.subjectPublic transportationpt_BR
dc.subjectSocial networkingpt_BR
dc.subjectVehiclespt_BR
dc.subjectSmart phonespt_BR
dc.subjectReal-time systemspt_BR
dc.titleRsRS: Ridesharing recommendation system based on social networks to improve the user’s QoEpt_BR
dc.typeArtigopt_BR
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