Implementation of Sequential Pattern Neural Classifier in E-Commerce Data Behavioral Characteristic Extraction

Abstract

Objectives: To present a framework for sequential pattern analysis using deep learning with behavioral characteristic extraction. This work intends to address the two major problems of accuracy and false positives predictions due to higher self-similarity in historical clicks. Methods: It implements sequenceaware recommenders for product recommendation using hybrid historical sequential pattern recommendation system (HSPRec) and hybrid sequential pattern based neural (HSPN) algorithm to take advantage of this crucial attribute. Data is gathered from Amazon, Flipkart, and other e-commerce sites. The simulation is carried out in Matlab. Findings: The proposed deep learning model provides 98% accuracy across 46 epochs, which is at least 8% higher, compared to existing works. The false positives in proposed solution are at least 6% lower compared to existing works. Novelty: The accuracy attained in recognizing consumer sequential patterns using HSPN is 95% and 98%, respectively. The computational effectiveness and viability of the neural network algorithm have been shown via our testing of the method.