An Architecture for Recommendation of Brazilian Artisanal Cheese Consumers
Abstract
Extracting information from social networks has become essential for the survival and modernization of many companies. With this purpose, this work presents an architecture capable of searching, analyzing and recommending the content and the propagation of information on social networks, considering the Brazilian dairy market. Using ontologies and inference mechanisms, the architecture is capable of supporting the classification of user content and present it through visualization mechanisms. Through this architecture, we aimed to support market research conducted at Embrapa Gado de Leite. The results obtained in a feasibility study were satisfactory and demonstrated that the architecture provides support to researchers.
Keywords:
recommender systems, social network, ontology, big data, data mining
References
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Tao, K., Abel, F., Gao, Q. & Houben, G.-J. (2012) “TUMS: Twitter-Based User Modeling Service Lecture Notes in Computer Science”, Springer Berlin Heidelberg, p. 1-15.
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Abu-Salih, B., Wongthongtham, P., & Chan, K. Y. (2018) “Twitter mining for ontologybased domain discovery incorporating machine learning”, Journal of Knowledge Management, p. 949-981.
Aggarwal, C. C. (2016) “An Introduction to Recommender Systems Recommender”, Springer International Publishing, p. 1-28.
Batmaz, Z.; Yurekli, A.; Bilge, A. & Kaleli, C. (2018) “A review on deep learning for recommender systems: challenges and remedies”, A.I. Review, p. 1-37.
Chen, Y., Yu, B., Zhang, X., & Yu, Y. (2016) “Topic modeling for evaluating students’ reflective writing: a case study of pre-service teachers’ journals”, Proceedings of the Sixth International Conference on LAK’16, ACM, p. 1-5.
Chianese, A.; Marulli, F. & Piccialli, F. (2016) “Cultural Heritage and Social Pulse: A Semantic Approach for CH Sensitivity Discovery in Social Media Data”, IEEE ICSC, p. 1-6.
Galletta, A., Carnevale, L., Celesti, A., Fazio, M., & Villari, M. (2018) “A Cloud-Based System for Improving Retention Marketing Loyalty Programs in Industry 4.0: A Study on Big Data Storage Implications”, IEEE Access, 6, 5485–5492.
Guarino, N. (1998) “Formal ontology in information systems”, Proceedings of the first international conference (FOIS'98), Trento, Italy IOS press, p. 3-15.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012) “Big data: the management revolution”, Harvard business review, p. 60–68.
Nardy, V. P. D. R., Carvalho, G. R. & da Rocha, D. T. (2019) “Mercado de leite fluido e queijos no Brasil: uma análise de 2005 a 2016”, Embrapa Gado de Leite, p. 1-5.
Nart, D. D., Degl'Innocenti, D., Basaldella, M., Agosti, M. & Tasso, C., (2016) “A Content-Based Approach to Social Network Analysis: A Case Study on Research Communities”, CCIS, Springer International Publishing, p. 142-154.
Pak, A., & Paroubek, P. (2010) “Twitter as a Corpus for Sentiment Analysis and Opinion Mining”, Proceedings of the 7th International Conference on LREC'10, p. 1320-1326.
Siqueira, K. B. (2019) “O mercado consumidor de leite e derivados”, Embrapa Gado de Leite - Circular Técnica (infoteca-e), p. 1-17.
Talamini, E. & Ferreira, G. M. V. (2010) “Merging netchain and social network: Introducing the social netchain concept as an analytical framework in the agribusiness sector”, African journal of business management, Academic Journals, p. 2981-2993.
Tao, K., Abel, F., Gao, Q. & Houben, G.-J. (2012) “TUMS: Twitter-Based User Modeling Service Lecture Notes in Computer Science”, Springer Berlin Heidelberg, p. 1-15.
Yang, X., Dong, M., Chen, X., & Ota, K. (2020) “Recommender System-Based Diffusion Inferring for Open Social Networks”, IEEE TCSS, p. 24–34.
Young, T., Hazarika, D., Poria, S. & Cambria, E. (2018) “Recent Trends in Deep Learning Based Natural Language Processing”, IEEE CIM, p. 55-75.
Published
2020-06-30
How to Cite
SOARES, Nedson D.; BRAGA, Regina; DAVID, José Maria N.; SIQUEIRA, Kennya B.; STRÖELE, Victor; CAMPOS, Fernanda.
An Architecture for Recommendation of Brazilian Artisanal Cheese Consumers. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 14. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 113-120.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2020.11189.
