Tourism Recommendation System using complex network approaches
Resumo
The amount of available data on the web has grown exponentially, mostly due to the emergence of the Collaborative Internet, in mid-2006, which turns the process of obtaining information into a hard task. This way, several computational techniques have been used in order to automate the exploitation and analysis of data, such as Text Mining techniques, Topic Modeling (TM), which establishes relationships between text documents and discussion topics through the present words, and Sentiment Analysis (SA), whose objective is to identify sentences' polarity; Complex Networks modeling, which seek to capture the dynamics of complex systems, present in social networks; and Recommendation Systems, which assist with decision making and whose operation resides in the suggestion of items that have not yet been evaluated by a user, such as traveling to a new place or trying another meal from a menu. The Tourism scenario is also included in the context of massive data generation and advances in techniques to deal with them. In this case, specialized travel platforms, like Tripadvisor, have a major role since they concentrate a large amount of data about users and their experience in Points-of-Interest (POI). Therefore, this work proposes a new approach to a predictive model for POI recommendation systems based on the construction of a Complex Network and the use of specific techniques for its structural analysis. The city chosen to validate these objectives was the city of Tiradentes, Minas Gerais, whose geographic proximity and tourism-oriented economy make it a good choice. The results obtained show that a predictive model based on Complex Networks does not overcome the error obtained by baseline algorithms, however, it brings a good ranking correlation between what was predicted and the real result, which makes it a good option for recommendation systems.
Palavras-chave:
Complex Networks, Graph mining, Recommendation Systems, Tourism
Referências
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Newman, M. Finding community structure in networks using the eigenvectors of matrices. Physical review. E, Statistical, nonlinear, and soft matter physics vol. 74 3 Pt 2, pp. 036104, 2006.
Pantano, E., Priporas, C.-V., Stylos, N., and Dennis, C. Facilitating tourists’ decision making through open data analyses: A novel recommender system. Tourism Management Perspectives vol. 31, pp. 323–331, 2019.
Qian, T., Liu, B., Nguyen, Q. V. H., and Yin, H. Spatiotemporal representation learning for translation-based poi recommendation. ACM Trans. Inf. Syst. 37 (2), Jan., 2019.
Ricci, F., Rokach, L., and Shapira, B. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, pp. 1–35, 2011.
Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S. Collaborative filtering recommender systems. In The adaptive web. Springer, pp. 291–324, 2007.
Zhao, P., Luo, A., Liu, Y., Zhuang, F., Xu, J., Li, Z., Sheng, V. S., and Zhou, X. Where to go next: A spatio-temporal gated network for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering, 2020.
Zheng, X., Luo, Y., Sun, L., Zhang, J., and Chen, F. A tourism destination recommender system using users’ sentiment and temporal dynamics. Journal of Intelligent Information Systems vol. 51, pp. 557–578, 2018.
Zheng, X., Xu, L., and Chai, S. Qos recommendation in cloud services. IEEE Access vol. 5, pp. 5171–5177, 2017.
Zou, Y., Donner, R. V., Marwan, N., Donges, J. F., and Kurths, J. Complex network approaches to nonlinear time series analysis. Physics Reports vol. 787, pp. 1–97, 2019.
Alves, A., Félix, L., Barbosa, C., Vieira, V., and Xavier, C. Tiradentes no tripadvisor - o que se fala sobre essa simpática cidade histórica? In Anais do XI Brazilian Workshop on Social Network Analysis and Mining. SBC, Porto Alegre, RS, Brasil, pp. 145–156, 2022.
Blondel, V., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment vol. 2008, pp. 10008, 2008.
Carvalho, D. V., Pereira, E. M., and Cardoso, J. S. Machine learning interpretability: A survey on methods and metrics. Electronics 8 (8): 832, 2019.
Cenamor, I., Rosa, T., Núñez, S., and Borrajo, D. Planning for tourism routes using social networks. Expert Syst. Appl. vol. 69, pp. 1–9, 2017.
Chen, T. T. and Lee, M. Research paper recommender systems on big scholarly data. In PKAW, 2018.
Cherifi, H., Palla, G., Szymanski, B., and Lu, X. On community structure in complex networks: challenges and opportunities. Applied Network Science vol. 4, pp. 1–35, 2019.
Clauset, A., Newman, M., and Moore, C. Finding community structure in very large networks. Physical review. E, Statistical, nonlinear, and soft matter physics vol. 70 6 Pt 2, pp. 066111, 2004.
Dascalu, M., Bodea, C., Mihailescu, M. N., Tanase, E. A., and Pablos, P. O. D. Educational recommender systems and their application in lifelong learning. Behaviour e Information Technology vol. 35, pp. 290 – 297, 2016.
Javed, M. A., Younis, M. S., Latif, S., Qadir, J., and Baig, A. Community detection in networks: A multidisciplinary review. Journal of Network and Computer Applications vol. 108, pp. 87–111, 2018.
Koren, Y. Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Transactions on Knowledge Discovery from Data (TKDD) 4 (1): 1–24, 2010.
Lancichinetti, A. and Fortunato, S. Community detection algorithms: a comparative analysis. Physical review. E, Statistical, nonlinear, and soft matter physics vol. 80 5 Pt 2, pp. 056117, 2009.
Lu, J., Wu, D., Mao, M., Wang, W., and Zhang, G. Recommender system application developments: a survey. Decision Support Systems vol. 74, pp. 12–32, 2015.
Miguéns, J., Baggio, R., and Costa, C. Social media and tourism destination: Tripadvisor case study, 2008.
Missaoui, S., Kassem, F., Viviani, M., Agostini, A., Faiz, R., and Pasi, G. Looker: a mobile, personalized recommender system in the tourism domain based on social media user-generated content. Personal and Ubiquitous Computing vol. 23, pp. 181–197, 2018.
Newman, M. Finding community structure in networks using the eigenvectors of matrices. Physical review. E, Statistical, nonlinear, and soft matter physics vol. 74 3 Pt 2, pp. 036104, 2006.
Pantano, E., Priporas, C.-V., Stylos, N., and Dennis, C. Facilitating tourists’ decision making through open data analyses: A novel recommender system. Tourism Management Perspectives vol. 31, pp. 323–331, 2019.
Qian, T., Liu, B., Nguyen, Q. V. H., and Yin, H. Spatiotemporal representation learning for translation-based poi recommendation. ACM Trans. Inf. Syst. 37 (2), Jan., 2019.
Ricci, F., Rokach, L., and Shapira, B. Introduction to recommender systems handbook. In Recommender systems handbook. Springer, pp. 1–35, 2011.
Schafer, J. B., Frankowski, D., Herlocker, J., and Sen, S. Collaborative filtering recommender systems. In The adaptive web. Springer, pp. 291–324, 2007.
Zhao, P., Luo, A., Liu, Y., Zhuang, F., Xu, J., Li, Z., Sheng, V. S., and Zhou, X. Where to go next: A spatio-temporal gated network for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering, 2020.
Zheng, X., Luo, Y., Sun, L., Zhang, J., and Chen, F. A tourism destination recommender system using users’ sentiment and temporal dynamics. Journal of Intelligent Information Systems vol. 51, pp. 557–578, 2018.
Zheng, X., Xu, L., and Chai, S. Qos recommendation in cloud services. IEEE Access vol. 5, pp. 5171–5177, 2017.
Zou, Y., Donner, R. V., Marwan, N., Donges, J. F., and Kurths, J. Complex network approaches to nonlinear time series analysis. Physics Reports vol. 787, pp. 1–97, 2019.
Publicado
28/11/2022
Como Citar
ALVES, Antônio P. S.; FÉLIX, Lucas G. S.; BARBOSA, Carlos Magno G.; VIEIRA, Vinícius da Fonseca; XAVIER, Carolina Ribeiro.
Tourism Recommendation System using complex network approaches. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 10. , 2022, Campinas/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 130-137.
ISSN 2763-8944.
DOI: https://doi.org/10.5753/kdmile.2022.227941.
