Product Recommendation System with Explicit Feedback Using Deep Learning Methods Açik Geri Bildirim ile Derin Öǧrenme Yöntemi Kullanilarak Ürün Öneri Sistemi

Kantepe E., Altıkardeş Z. A. , Erdal H.

2020 Innovations in Intelligent Systems and Applications Conference, ASYU 2020, İstanbul, Turkey, 15 - 17 October 2020 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/asyu50717.2020.9259814
  • City: İstanbul
  • Country: Turkey
  • Keywords: autoencoder, deep learning, optimization algorithms, recommendation system


© 2020 IEEE.Today, efforts are made to develop and improve recommendation systems that will direct users to the right product according to their individual preferences during internet shopping. In this study, the recommendation system was designed with Autoencoders, which are one of the methods of deep learning and MovieLens dataset. While designing the system, various optimization algorithms, namely Gradient Descent, Gradient Descent with Momentum, RmsProp and Adam (Adaptive Momentum Optimization), were tried by using TensorFlow in the Python programming language. Moreover, the effect of increasing the amount of the data on the optimization algorithm was analyzed. Consequently, it was effectively demonstrated that the most successful one was the Adam algorithm with a test error of 1.363. It was also observed that decreasing the sparsity on the training data leads to a lower test error.