Predicting mobility patterns based on profiles of social media users: tourists case study

• Ano de defesa: 2022
• Autor(a) principal: Senefonte, Helen Cristina de Mattos
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial; UTFPR
• Palavras-chave em Português: Redes sociais on-line - Localização; Mineração de dados (Computação); Aprendizado do computador; Turistas - Estudo de casos; Sistemas de reconhecimento de padrões; Algorítmos computacionais; Turismo; Online social networks - Location; Data mining; Machine learning; Tourists - Case studies; Pattern recognition systems; Computer algorithms; Tourism; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/29793

Studies based on traditional data sources like surveys, for instance, offer poor scalability. The experiments are limited, and the results are restricted to small regions (such as a city or a state). The use of location-based social network (LBSN) data can mitigate the scalability problem by enabling the study of social behavior in large populations. When explored with Data Mining and Machine Learning techniques, LBSN data can be used to provide predictions of relevant cultural and behavioral data from cities or countries around the world. The main goal of this work is to predict and explore user behavior from LBSNs in the context of tourists’ mobility patterns. To achieve this goal, we propose PredicTour, which is an approach used to process LBSN users’ check-ins and to predict mobility patterns of tourists with or without previous visiting records when visiting new countries. PredicTour is composed of three key blocks: mobility modeling, profile extraction, and tourist’ mobility prediction. In the first block, sequences of check-ins in a time interval are associated with other user information to produce a new structure called “mobility descriptor”. In the profile extraction, self-organizing maps and fuzzy C-means work together to group users according to their mobility descriptors. PredicTour then identifies tourist’ profiles and estimates their mobility patterns in new countries. When comparing the performance of PredicTour with three well-known machine learning-based models, the results indicate that PredicTour outperforms the baseline approaches. Therefore, it is a good alternative for predicting and understanding international tourists’ mobility, which has an economic impact on the tourism industry, particularly when services and logistics across international borders should be provided. The proposed approach can be used in different applications such as recommender systems for tourists, and decision-making support for urban planners interested in improving both the tourists’ experience and attractiveness of venues through personalized services.