An Efficient Recommendation System in E-commerce using Passer learning optimization based on Bi-LSTM

TL;DR

This paper presents a Passer Learning Optimization-enhanced Bi-LSTM recommendation system that improves accuracy and efficiency in e-commerce by effectively analyzing reviews, achieving low MSE and high F1 scores across multiple datasets.

Contribution

It introduces a novel Passer Learning Optimization method integrated with Bi-LSTM for e-commerce recommendations, enhancing performance over existing models.

Findings

Achieves 1.24% MSE on baby dataset

F1 scores of 88.46% and 92.51% on digital music and patio lawn datasets

Demonstrates robustness and adaptability across various e-commerce domains

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 Passer Learning Optimization-enhanced Bi-LSTM classifier applicable to e-commerce recommendation systems with improved accuracy and efficiency compared to state-of-the-art models. It achieves as low as 1.24% MSE on the baby dataset. This lifts it as high as 88.58%. Besides, there is also robust performance of the system on digital music and patio lawn garden datasets at F1 of 88.46% and 92.51%, correspondingly. These results, made possible by advanced graph embedding for effective knowledge extraction and fine-tuning of classifier parameters, establish the suitability of the proposed model in various e-commerce environments.