Bayesian Approach to News Recommendation Systems

Autores

  • Jossandro Balardin Silva Universidade de Santa Cruz do Sul
  • Jacques Nelson Corleta Schreiber Universidade de Santa Cruz do Sul
  • Elpídio Oscar Benitez Nara Universidade de Santa Cruz do Sul

Palavras-chave:

Bayesian network, Clustering, Online news, Recommender system.

Resumo

This research was responsible for the development of a method for recommending news in online newspapers. This study takes into consideration that each reader has specific needs and interests when reading online newspapers, and it is a challenge to bring personalized and individualized information, in order to meet each reader's needs. The main goal here was solving or minimizing this problem when there is a new reader, because the system has little or no information over the reader’s preferences. This descriptive research used as a subject a new reader from a news portal and all data collected from the web browsing experience was performed without that user’s knowledge. The research may be characterized as applied, since it generated knowledge enough for solving the problem of online newspaper readers. A quantitative approach was adopted, because the news recommended by the system were classified and the system’s accuracy was quantified comparing the system`s suggestions and the decisions made by the readers. The solution adopted involved accessing three different methods. The Bayesian network was adopted as the main method when generating news suggestions to the new reader and the excess of variables was clustered using the K-means algorithm. The probabilities missing on this network were captured through the EM algorithm (Expectation Maximization). This algorithm uses cases in which variables were used to learn how to predict their values when they are not being observed.

Biografia do Autor

Jossandro Balardin Silva, Universidade de Santa Cruz do Sul

Masters in Industrial Systems andProcesses from the University of Santa Cruz do Sul - UNISC, Lecturer in theDepartment of Computer UNISC, Faculty of Technical course in computer CEPRU and project manager at Gaz Portal Gazette Communications Group.

Jacques Nelson Corleta Schreiber, Universidade de Santa Cruz do Sul

Doctoral degree in Production Engineering from the Federal University of Santa Catarina - UFSC, Reviewer of the National Institute of Educational Studies and Research and associate teacher at the University of Santa Cruz do Sul - UNISC.

Elpídio Oscar Benitez Nara, Universidade de Santa Cruz do Sul

Doctoral degree in Quality Management and Productivity from the Federal University of Santa Catarina - UFSC, Assistant Teacher at the University of Santa Cruz do Sul - UNISC, Industrial Director of Rebelli.

Downloads

Publicado

26/06/2017

Como Citar

Silva, J. B., Schreiber, J. N. C., & Nara, E. O. B. (2017). Bayesian Approach to News Recommendation Systems. Ciência Da Informação, 44(3). Recuperado de http://revista.ibict.br/ciinf/article/view/1902

Edição

Seção

Artigos