Target Advertising Classification using Combination of Deep Learning and Text model

E. Phaisangittisagul, Y. Koobkrabee, K. Wirojborisuth, T. Ratanasrimetha, S. Aummaro
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引用次数: 3

Abstract

In recent years, there has been a great interest in online advertising not only to promote products and services but to build a brand of the company as well. To satisfy customer needs, some businesses apply intelligent technology to advertise their products and services based on customer interests. Other advertisers allow customers or members to upload their promotions using image and/or message to advertise their businesses and services. However, filtering of promotional advertising is an essential part to detect improper information before posting on the websites and social media. As a result, a model to classify promotional advertising is proposed to identify whether relevant promotion content for a specific business or service in order to meet precise customers’ attention. The proposed algorithm in this study based on deep learning is designed to handle promotional image and message in competition with the 2nd KU Data Science Boot Camp 2018. Its performance is evaluated on the promotional advertising data provided by Wongnai. Finally, the accuracy of the proposed method can achieve satisfactory performance of 82.95% in testing data.
基于深度学习和文本模型的目标广告分类
近年来,人们对在线广告产生了极大的兴趣,不仅是为了推广产品和服务,也是为了建立公司的品牌。为了满足客户的需求,一些企业应用智能技术根据客户的兴趣来宣传他们的产品和服务。其他广告商允许客户或会员上传他们的促销活动,使用图像和/或信息来宣传他们的业务和服务。然而,在网站和社交媒体上发布促销广告之前,过滤促销广告是检测不当信息的重要组成部分。因此,提出了一种对促销广告进行分类的模型,以确定促销内容是否与特定的业务或服务相关,从而满足精确的客户关注。本研究中提出的基于深度学习的算法旨在与2018年第二届KU数据科学训练营竞争,以处理促销图像和信息。它的表现是根据旺奈提供的促销广告数据进行评估的。最后,在测试数据中,该方法的准确率达到了82.95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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