Authors:
Igor Lampa
;
Vitoria Gomes
and
Geraldo Zafalon
Affiliation:
Department of Computer Science and Statistics, Universidade Estadual Paulista (UNESP), Rua Cristóvão Colombo, 2265, Jardim Nazareth, São José do Rio Preto - SP, 15054-000, Brazil
Keyword(s):
Recommendation Systems, Deep Learning, Collaborative Filtering, Content-Based, Hybrid Approach.
Abstract:
The massive use of the digital platforms has provided an exponential increase at the amount of data consumed and daily generated. Thus, there is a data overload which directly affects the consume experience of digital products, whether at find a news, consume an e-commerce product or to choose a movie in a streaming platform. In this context, emerge the recommendation systems, which have the finality of provide an efficient way to comprehend the user predilections and to recommend direct items. Thus, this work brings the classical concepts and techniques already used, as well as analyzes their use along with deep learning, which through evaluated results has a grater capability to obtain implicit relationships between users and items, providing recommendations with better quality and accuracy. Furthermore, considering the review of the literature and analysis provided, an architectural model for recommendation system based on deep learning is proposed, which is defined as a hybrid sy
stem.
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