An Efficient Recommendation System in E-commerce Using Passer Learning Optimization Based on Bi-LSTM

Hemn Barzan Abdalla; Mehdi Gheisari; Awder Ahmed; Bahtiyar Mehmed; Maryam Cheraghy; Yang  Liu

doi:10.47852/bonviewJCCE52025879

Authors

Hemn Barzan Abdalla Department of Computer Science, Wenzhou-Kean University, China and Department of Computer Science and Technology, Kean University, USA https://orcid.org/0000-0002-3637-0447
Mehdi Gheisari Institute of Artificial Intelligence, Shaoxing University, China https://orcid.org/0000-0002-5643-0021
Awder Ahmed Technical College of Engineering, Sulaimani Polytechnic University, Iraq https://orcid.org/0000-0001-5054-5584
Bahtiyar Mehmed School of Economics, Neusoft Institute Guangdong, China https://orcid.org/0000-0002-2983-862X
Maryam Cheraghy Department of Computer Science, Wenzhou-Kean University, China and Department of Computer Science and Technology, Kean University, USA https://orcid.org/0000-0001-7035-1795
Yang Liu Department of Computer Science, Swansea University, UK https://orcid.org/0000-0003-2486-5765

DOI:

https://doi.org/10.47852/bonviewJCCE52025879

Keywords:

recommendation system, e-commerce, Passer Learning Optimization, Bi-LSTM classifier, TF-IDF

Abstract

Online reviews play a crucial role in shaping consumer decisions, especially in the context of e-commerce. However, the quality and reliability of these reviews can vary significantly. Some reviews contain misleading or unhelpful information, such as advertisements, fake content, or irrelevant details. These issues pose significant challenges for recommendation systems, which rely on user-generated reviews to provide personalized suggestions. This article introduces a recommendation system based on a Passer Learning Optimization-enhanced Bidirectional Long Short-Term Memory network classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. More specifically, the proposed model achieves a high accuracy of 98.03%, F1 score of 98.03%, precision of 98.49%, recall of 97.57%, and minimum mean square error of 1.97 based on training percentage using the patio lawn garden dataset. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters by the hybrid PLO algorithm, establish the suitability of the proposed PLO-Bi-LSTM model in various e-commerce environments.

Received: 9 April 2025 | Revised: 26 June 2025 | Accepted: 24 August 2025

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The Amazon product data that support the findings of this study are openly available at https://cseweb.ucsd.edu/~jmcauley/datasets/amazon/links.html.

Author Contribution Statement

Hemn Barzan Abdalla: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Mehdi Gheisari: Conceptualization, Software, Data curation. Awder Ahmed: Conceptualization, Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization. Bahtiyar Mehmed: Formal analysis, Investigation, Resources. Maryam Cheraghy: Validation, Resources, Writing – review & editing. Yang Liu: Validation, Investigation, Resources.