逻辑挖掘方法:基于进化算法的购物者购买数据提取

Q4 Computer Science
Mohd Shareduwan Mohd Kasihmuddin, Nur Shahira Abdul Halim, Siti Zulaikha Mohd Jamaludin, M. Mansor, Alyaa Alway, Nur Ezlin Zamri, Siti Aishah Azhar, Muhammad Fadhil Marsani
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引用次数: 0

摘要

网上购物在全球是一个价值数十亿美元的产业。然而,与购买意愿相关的一些挑战会影响电子商务的销售。例如,电子商务平台无法识别哪些因素促成了产品的高销量。此外,网上卖家很难找到符合消费者偏好的产品。因此,本工作将利用人工神经网络为网上购物行业或电子商务平台提供知识提取,从而可能提高其销售和服务。在网上购物领域,利用神经网络模型提取知识的尝试有限,尤其是围绕网上购物者购买意愿的研究。本研究采用2-满意逻辑来表示购物属性,并采用一种特殊的递归人工神经网络Hopfield神经网络。为了降低学习复杂性,在执行基于2-满意度的反向分析方法时,在整个学习阶段实施了遗传算法来优化逻辑规则,并在学习阶段实现了该方法的比较。根据所选择的性能评价指标,对基于2-满意度反向分析的遗传算法进行性能评价。仿真结果表明,该模型在对在线购物者数据集进行逻辑挖掘方面优于现有模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Logic Mining Approach: Shoppers’ Purchasing Data Extraction via Evolutionary Algorithm
Online shopping is a multi-billion-dollar industry worldwide. However, several challenges related to purchase intention can impact the sales of e-commerce. For example, e-commerce platforms are unable to identify which factors contribute to the high sales of a product. Besides, online sellers have difficulty finding products that align with customers’ preferences. Therefore, this work will utilize an artificial neural network to provide knowledge extraction for the online shopping industry or e-commerce platforms that might improve their sales and services. There are limited attempts to propose knowledge extraction with neural network models in the online shopping field, especially research revolving around online shoppers’ purchasing intentions. In this study, 2-satisfiability logic was used to represent the shopping attribute and a special recurrent artificial neural network named Hopfield neural network was employed. In reducing the learning complexity, a genetic algorithm was implemented to optimize the logical rule throughout the learning phase in performing a 2-satisfiability-based reverse analysis method, implemented during the learning phase as this method was compared. The performance of the genetic algorithm with 2-satisfiability-based reverse analysis was measured according to the selected performance evaluation metrics. The simulation suggested that the proposed model outperformed the existing model in doing logic mining for the online shoppers dataset. 
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
95
期刊介绍: IJICT is a refereed journal in the field of information and communication technology (ICT), providing an international forum for professionals, engineers and researchers. IJICT reports the new paradigms in this emerging field of technology and envisions the future developments in the frontier areas. The journal addresses issues for the vertical and horizontal applications in this area. Topics covered include: -Information theory/coding- Information/IT/network security, standards, applications- Internet/web based systems/products- Data mining/warehousing- Network planning, design, administration- Sensor/ad hoc networks- Human-computer intelligent interaction, AI- Computational linguistics, digital speech- Distributed/cooperative media- Interactive communication media/content- Social interaction, mobile communications- Signal representation/processing, image processing- Virtual reality, cyber law, e-governance- Microprocessor interfacing, hardware design- Control of industrial processes, ERP/CRM/SCM
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