Comparison of LSTM, GRU and Hybrid Architectures for usage of Deep Learning on Recommendation Systems
MARIO LEANDRO PIRES TOLEDO, Marcelo Novaes de Rezende · 2020
This article shows the results of a performance analysis from LSTM, GRU and Hybrid Neural Network architectures in Recommendation Systems. To this end, prototypes of the networks were built to be trained using data from the user's browsing history of a streaming website in China. The results were evaluated using the metrics of Accuracy, Precision, Recall and F1-Score, thus identifying the advantages and disadvantages of each architecture in different approaches.