指导网络广告创意设计

Shaunak Mishra, Manisha Verma, J. Gligorijevic
{"title":"指导网络广告创意设计","authors":"Shaunak Mishra, Manisha Verma, J. Gligorijevic","doi":"10.1145/3298689.3347022","DOIUrl":null,"url":null,"abstract":"Ad creatives (text and images) for a brand play an influential role in online advertising. To design impactful ads, creative strategists employed by the brands (advertisers) typically go through a time consuming process of market research and ideation. Such a process may involve knowing more about the brand, and drawing inspiration from prior successful creatives for the brand, and its competitors in the same product category. To assist strategists towards faster creative development, we introduce a recommender system which provides a list of desirable keywords for a given brand. Such keywords can serve as underlying themes, and guide the strategist in finalizing the image and text for the brand's ad creative. We explore the potential of distributed representations of Wikipedia pages along with a labeled dataset of keywords for 900 brands by using deep relevance matching for recommending a list of keywords for a given brand. Our experiments demonstrate the efficacy of the proposed recommender system over several baselines for relevance matching; although end-to-end automation of ad creative development still remains an open problem in the advertising industry, the proposed recommender system is a stepping stone by providing valuable insights to creative strategists and advertisers.","PeriodicalId":215384,"journal":{"name":"Proceedings of the 13th ACM Conference on Recommender Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Guiding creative design in online advertising\",\"authors\":\"Shaunak Mishra, Manisha Verma, J. Gligorijevic\",\"doi\":\"10.1145/3298689.3347022\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Ad creatives (text and images) for a brand play an influential role in online advertising. To design impactful ads, creative strategists employed by the brands (advertisers) typically go through a time consuming process of market research and ideation. Such a process may involve knowing more about the brand, and drawing inspiration from prior successful creatives for the brand, and its competitors in the same product category. To assist strategists towards faster creative development, we introduce a recommender system which provides a list of desirable keywords for a given brand. Such keywords can serve as underlying themes, and guide the strategist in finalizing the image and text for the brand's ad creative. We explore the potential of distributed representations of Wikipedia pages along with a labeled dataset of keywords for 900 brands by using deep relevance matching for recommending a list of keywords for a given brand. Our experiments demonstrate the efficacy of the proposed recommender system over several baselines for relevance matching; although end-to-end automation of ad creative development still remains an open problem in the advertising industry, the proposed recommender system is a stepping stone by providing valuable insights to creative strategists and advertisers.\",\"PeriodicalId\":215384,\"journal\":{\"name\":\"Proceedings of the 13th ACM Conference on Recommender Systems\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 13th ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3298689.3347022\",\"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 13th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3298689.3347022","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

品牌的广告创意(文字和图片)在网络广告中发挥着重要作用。为了设计有影响力的广告,品牌(广告商)雇用的创意战略家通常要经历一个耗时的市场研究和创意过程。这个过程可能涉及更多地了解品牌,并从之前成功的品牌创意中汲取灵感,以及在同一产品类别中的竞争对手。为了帮助战略家更快地进行创意开发,我们引入了一个推荐系统,该系统为给定品牌提供了一个理想关键词列表。这些关键词可以作为潜在的主题,指导策划师最终确定品牌广告创意的图像和文本。我们通过使用深度关联匹配来推荐给定品牌的关键字列表,探索维基百科页面的分布式表示的潜力,以及900个品牌的标记关键字数据集。我们的实验证明了所提出的推荐系统在多个相关匹配基线上的有效性;虽然广告创意开发的端到端自动化仍然是广告行业的一个开放问题,但提议的推荐系统是一个跳板,可以为创意战略家和广告商提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Guiding creative design in online advertising
Ad creatives (text and images) for a brand play an influential role in online advertising. To design impactful ads, creative strategists employed by the brands (advertisers) typically go through a time consuming process of market research and ideation. Such a process may involve knowing more about the brand, and drawing inspiration from prior successful creatives for the brand, and its competitors in the same product category. To assist strategists towards faster creative development, we introduce a recommender system which provides a list of desirable keywords for a given brand. Such keywords can serve as underlying themes, and guide the strategist in finalizing the image and text for the brand's ad creative. We explore the potential of distributed representations of Wikipedia pages along with a labeled dataset of keywords for 900 brands by using deep relevance matching for recommending a list of keywords for a given brand. Our experiments demonstrate the efficacy of the proposed recommender system over several baselines for relevance matching; although end-to-end automation of ad creative development still remains an open problem in the advertising industry, the proposed recommender system is a stepping stone by providing valuable insights to creative strategists and advertisers.
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
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:604180095
Book学术官方微信