Theory and Practice in Recommender Systems

• Autor(a) principal: Azambuja, Rogério Xavier de
• Data de Publicação: 2021
• Outros Autores: Morais, A. Jorge, Filipe, Vítor
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: https://doi.org/10.34627/rcc.v15i0.264

In recent decades, artificial intelligence use has been frequent in the computational applications development. More recently, machine learning, especially through the use of deep learning, has driven growth and expanded the intelligent systems development for different domains. In the current scenario of technological growth, the recommender systems appear with increasing frequency through their different techniques for information filtering in large datasets. It is a challenge to provide adaptive recommendation to mitigate information overload in online environments. This article reviews previous works and addresses some of the theoretical-conceptual and theoretical-practical aspects that constitute the recommender systems, characterizing the use of deep neural network (DNN) to provide sequential recommendation supported by session-based recommendation.