基于广告欺诈检测的广告主管理决策支持系统

IF 3.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Artificial Intelligence and Soft Computing Research Pub Date : 2021-10-01 DOI:10.2478/jaiscr-2021-0020
Marcin Gabryel, Magdalena M. Scherer, Ł. Sułkowski, Robertas Damaševičius
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引用次数: 5

摘要

摘要高效的潜在客户管理可以大大增强在线渠道营销计划。在本文中,我们将网站流量分为人类流量和机器人流量。我们使用带有嵌入层的前馈神经网络。此外,我们对分类数据使用一个热编码。鼠标点击数据来自7家大型零售店,线索分类数据来自3家金融机构。数据是通过嵌入HTML页面的JavaScript代码收集的。所提出的三个模型在检测人工生成的流量方面实现了相对较高的精度。
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Decision Making Support System for Managing Advertisers By Ad Fraud Detection
Abstract Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.
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来源期刊
Journal of Artificial Intelligence and Soft Computing Research
Journal of Artificial Intelligence and Soft Computing Research COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
7.00
自引率
25.00%
发文量
10
审稿时长
24 weeks
期刊介绍: Journal of Artificial Intelligence and Soft Computing Research (available also at Sciendo (De Gruyter)) is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.
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