ALARM: A Light Application for Recommendation and Monitoring
Resumo
Big companies usually have human and financial resources to personalize their websites. On the other hand, small and medium-sized companies usually do not have such resources. In this paper we propose ALARM: A Light Application for Recommendation and Monitoring. This free platform enables automatic recommendations and monitoring in small and medium-sized websites. The platform is independent of the site structure, as well as monitoring and recommendation methods which may be used in it. We illustrate the features of the platform in a case study, where we show how it can be used to provide recommendations as well as to analyze them.
Referências
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M. Deshpande and G. Karypis. 2004. Item-based top-N Recommendation Algorithms. ACM Transactions on Information Systems 22, 1 (2004), 143–177.
Marcos Aurélio Domingues, Carlos Soares, and Alípio M. Jorge. 2012. Using statistics, visualization and data mining for monitoring the quality of meta-data in web portals. Information Systems and e-Business Management, 569–595.
Alípio M. Jorge, João Vinagre, Marcos Aurélio Domingues, João Gama, CarlosSoares, Pawel Matuszyk, and Myra Spiliopoulou. 2016. Scalable Online Top-NRecommender Systems. In E-Commerce and Web Technologies - 17th International Conference, EC-Web 2016, Porto, Portugal, Revised Selected Papers. 3–20.
Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul Kantor. 2010. Recommender Systems Handbook. Springer-Verlag New York, New York, NY, USA.
Ana Ribeiro Carneiro, Alípio M. Jorge, Pedro Quelhas Brito, and Marcos Aurélio Domingues. 2014. Measuring the Effectiveness of an E-Commerce Site Through Web and Sales Activity. Proceedings in Mathematics Statistics. 1ed.: Springer International Publishing, 149–162.
M. Deshpande and G. Karypis. 2004. Item-based top-N Recommendation Algorithms. ACM Transactions on Information Systems 22, 1 (2004), 143–177.
Marcos Aurélio Domingues, Carlos Soares, and Alípio M. Jorge. 2012. Using statistics, visualization and data mining for monitoring the quality of meta-data in web portals. Information Systems and e-Business Management, 569–595.
Alípio M. Jorge, João Vinagre, Marcos Aurélio Domingues, João Gama, CarlosSoares, Pawel Matuszyk, and Myra Spiliopoulou. 2016. Scalable Online Top-NRecommender Systems. In E-Commerce and Web Technologies - 17th International Conference, EC-Web 2016, Porto, Portugal, Revised Selected Papers. 3–20.
Francesco Ricci, Lior Rokach, Bracha Shapira, and Paul Kantor. 2010. Recommender Systems Handbook. Springer-Verlag New York, New York, NY, USA.
Publicado
30/11/2020
Como Citar
BATISTA, Arthur Rodrigues; PEREIRA, Luiz Fellipe Machi ; DOMINGUES, Marcos Aurélio.
ALARM: A Light Application for Recommendation and Monitoring. In: WORKSHOP DE TRABALHOS DE INICIAÇÃO CIENTÍFICA - SIMPÓSIO BRASILEIRO DE SISTEMAS MULTIMÍDIA E WEB (WEBMEDIA), 26. , 2020, São Luís.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 37-40.
ISSN 2596-1683.
DOI: https://doi.org/10.5753/webmedia_estendido.2020.13058.
