An improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS)

6Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

Abstract

In recent years, information overload has become a phenomenon where it makes people difficult to filter relevant information. To address issues such as high-dimensional data, cold start, and data sparsity, semi-autoencoder is one of the unsupervised deep learning methods used in the recommendation systems. It is particularly useful for reducing data dimensions, capturing latent representations, and flexibly reconstructing various parts of input data. In this article, we propose an improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS) framework. This method aims to show better performance of the hybrid collaborative recommendation via semi-autoencoder (HRSA) technique. Two novel elements for iHSARS's architecture have been introduced. The first element is an increase sources of side information of the input layer, while the second element is the number of hidden layers has been expanded. To verify the improvement of the model, MovieLens-100K and MovieLens-1M datasets have been applied to the model. The comparison between the proposed model and different state-of-the-art methods has been carried using mean absolute error (MAE) and root mean square error (RMSE) metrics. The experiments demonstrate that our framework improved the efficiency of the recommendation system better than others.

Cite

CITATION STYLE

APA

Al-Sbou, A. M., & Rahim, N. H. A. (2023). An improved hybrid semi-stacked autoencoder for item-features of recommendation system (iHSARS). Indonesian Journal of Electrical Engineering and Computer Science, 30(1), 481–490. https://doi.org/10.11591/ijeecs.v30.i1.pp481-490

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free