SoCraft: Advertiser-level Predictive Scoring for Creative Performance on Meta

Alfred Huang, Qi Yang, Sergey I. Nikolenko, Marlo Ongpin, Ilia Gossoudarev, Ngoc Yen Duong, Kirill Lepikhin, Sergey Vishnyakov, Chu-Farseeva Yuyi, Aleksandr Farseev
{"title":"SoCraft: Advertiser-level Predictive Scoring for Creative Performance on Meta","authors":"Alfred Huang, Qi Yang, Sergey I. Nikolenko, Marlo Ongpin, Ilia Gossoudarev, Ngoc Yen Duong, Kirill Lepikhin, Sergey Vishnyakov, Chu-Farseeva Yuyi, Aleksandr Farseev","doi":"10.1145/3539597.3573032","DOIUrl":null,"url":null,"abstract":"In this technical demonstration, we present SoCraft, a framework to build an advertiser-level multimedia ad content scoring platform for Meta Ads. The system utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high- and low-level features of its contextual data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore.","PeriodicalId":227804,"journal":{"name":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","volume":"104 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3539597.3573032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In this technical demonstration, we present SoCraft, a framework to build an advertiser-level multimedia ad content scoring platform for Meta Ads. The system utilizes a multimodal deep neural architecture to score and evaluate advertised content on Meta using both high- and low-level features of its contextual data such as text, image, targeting, and ad settings. In this demo, we present two deep models, SoDeep and SoWide, and validate the effectiveness of SoCraft with a successful real-world case study in Singapore.
SoCraft:广告商层面的Meta创意表现预测评分
在这个技术演示中,我们展示了SoCraft,一个为Meta广告构建广告商级多媒体广告内容评分平台的框架。该系统利用多模态深度神经架构对Meta上的广告内容进行评分和评估,并使用其上下文数据(如文本、图像、目标定位和广告设置)的高级和低级特征。在这个演示中,我们展示了两个深度模型,SoDeep和SoWide,并通过新加坡的一个成功的现实案例研究验证了SoCraft的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信