In the current competitive corporate world, organizations rely on their products’ advertisements for surpassing competitors in reaching out to a larger pool of customers. This forces companies to focus on advertisement quality. This work presents a content-based advertisement viewability prediction framework using Artificial Intelligence (AI) methods. The primary focus here is on the web-advertisements available on various online shopping websites. Most of the past work in this domain emphasizes on the scroll depth and dwell time of an ad. However, the features that directly influence the viewability of an ad have been overlooked in the past. Unlike other approaches, this work considers multiple in-ad features that directly influence its viewability. Some of these include color, urgency, language, offers, discount, type, and prominent gender. This work presents an AI-based framework for identifying the features attributing towards increased viewability of ads. Feature selection techniques are executed on the dataset to extract important attributes. Afterward, clustering is applied to confirm the number of class labels assigned to the instances. To validate the clustering results, three validation indices are used here, namely Davies Bouldin Index, Dunn Index, and Silhouette Coefficient. Five classifiers, i.e., Support Vector Machine, k- Nearest Neighbors, Artificial Neural Network, Random Forest, and Gradient Regression Boosting Trees are trained using multiple features and viewability of an ad is predicted. The obtained results confirm that various in-content ad features, i.e., gender, type, discount, layout, and crowdedness play a vital role in predicting an ad’s viewability.

在当今竞争激烈的企业界，组织依靠其产品的广告来超越竞争对手，从而吸引更大的客户群。这迫使公司专注于广告质量。这项工作提出了使用人工智能（AI）方法的基于内容的广告可见度预测框架。这里的主要重点是各种在线购物网站上可用的网络广告。该领域过去的大部分工作都着重于广告的滚动深度和停留时间。但是，过去直接忽略了直接影响广告可见度的功能。与其他方法不同，这项工作考虑了直接影响其可见度的多个广告内功能。其中一些包括颜色，紧迫性，语言，要约，折扣，类型和突出性别。这项工作提出了一个基于AI的框架，用于识别归因于广告可见度提高的功能。对数据集执行特征选择技术以提取重要属性。之后，应用聚类以确认分配给实例的类标签的数量。为了验证聚类结果，此处使用了三个验证指标，即Davies Bouldin指标，Dunn指标和Silhouette系数。五个分类器，即支持向量机，这里使用三个验证指标，即Davies Bouldin指标，Dunn指标和Silhouette系数。五个分类器，即支持向量机，这里使用三个验证指标，即Davies Bouldin指标，Dunn指标和Silhouette系数。五个分类器，即支持向量机，使用多个功能来训练k-最近邻，人工神经网络，随机森林和梯度回归助推树，并预测广告的可见度。所获得的结果证实，各种内容内广告特征（即性别，类型，折扣，布局和拥挤程度）在预测广告的可见度方面起着至关重要的作用。