SoCraft:广告商层面的Meta创意表现预测评分

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining Pub Date : 2023-02-27 DOI:10.1145/3539597.3573032

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:广告商层面的Meta创意表现预测评分","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":"{\"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}","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

摘要

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。

查看原文本刊更多论文

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

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining

自引率

0.00%

发文量