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Guiding creative design in online advertising

Published:10 September 2019Publication History

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.

References

  1. Adobe creative cloud stock photos. https://www.adobe.com/creativecloud/stock.html.Google ScholarGoogle Scholar
  2. Automatic understanding of image and video advertisements. http://people.cs.pitt.edu/~kovashka/ads.Google ScholarGoogle Scholar
  3. Digital advertising. https://www.statista.com/outlook/216/100/digital-advertising/worldwide.Google ScholarGoogle Scholar
  4. Match zoo. https://github.com/NTMC-Community/MatchZoo.Google ScholarGoogle Scholar
  5. NLTK SentiWordNet. http://www.nltk.org/howto/sentiwordnet.html.Google ScholarGoogle Scholar
  6. Vader sentiment. https://github.com/cjhutto/vaderSentiment.Google ScholarGoogle Scholar
  7. Vowpal wabbit. https://github.com/JohnLangford/vowpal_wabbit/wiki.Google ScholarGoogle Scholar
  8. Wikipedia infobox company template. https://en.wikipedia.org/wiki/Template:Infobox_company.Google ScholarGoogle Scholar
  9. S. Baccianella, A. Esuli, and F. Sebastiani. Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Lrec, volume 10, pages 2200--2204, 2010.Google ScholarGoogle Scholar
  10. N. Bhamidipati, R. Kant, S. Mishra, and M. Zhu. A large scale prediction engine for app install clicks and conversions. In CIKM 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. E. Colleoni, A. Arvidsson, L. K. Hansen, and A. Marchesini. Measuring corporate reputation using sentiment analysis. In Proceedings of the 15th International Conference on Corporate Reputation: Navigating the Reputation Economy, New Orleans, USA, 2011.Google ScholarGoogle Scholar
  12. M. Ghiassi, J. Skinner, and D. Zimbra. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with applications, 40(16):6266--6282, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. C. H. E. Gilbert. Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth International Conference on Weblogs and Social Media (ICWSM-14). Available at (20/04/16) http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf, 2014.Google ScholarGoogle Scholar
  14. J. Gligorijevic, D. Gligorijevic, I. Stojkovic, X. Bai, A. Goyal, and Z. Obradovic. Deeply supervised model for click-through rate prediction in sponsored search. Data Mining and Knowledge Discovery, 2019.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. J. Guo, Y. Fan, Q. Ai, and W. B. Croft. A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T.-S. Chua. Neural collaborative filtering. In WWW, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Z. Hussain, M. Zhang, X. Zhang, K. Ye, C. Thomas, Z. Agha, N. Ong, and A. Kovashka. Automatic understanding of image and video advertisements. In CVPR, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  18. B. J. Jansen, M. Zhang, K. Sobel, and A. Chowdury. Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11):2169--2188, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Y. Koren. Factorization meets the neighborhood: A multifaceted collaborative filtering model. In KDD, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Q. Le and T. Mikolov. Distributed representations of sentences and documents. ICML'14, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. W. Li, X. Wang, R. Zhang, Y. Cui, J. Mao, and R. Jin. Exploitation and exploration in a performance based contextual advertising system. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Z. Lin, M. Feng, C. N. d. Santos, M. Yu, B. Xiang, B. Zhou, and Y. Bengio. A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130, 2017.Google ScholarGoogle Scholar
  23. H. B. McMahan, G. Holt, D. Sculley, M. Young, D. Ebner, J. Grady, L. Nie, T. Phillips, E. Davydov, D. Golovin, S. Chikkerur, D. Liu, M. Wattenberg, A. M. Hrafnkelsson, T. Boulos, and J. Kubica. Ad click prediction: a view from the trenches. KDD 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. S. Mishra, A. Pappu, and N. Bhamidipati. Inferring advertiser sentiment in online articles using wikipedia footnotes. In Companion Proceedings of The 2019 World Wide Web Conference, WWW '19, 2019. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. T. Munkhdalai and H. Yu. Neural semantic encoders. In Proceedings of the conference. Association for Computational Linguistics. Meeting, volume 1, page 397. NIH Public Access, 2017.Google ScholarGoogle Scholar
  26. A. Radford, R. Jozefowicz, and I. Sutskever. Learning to generate reviews and discovering sentiment. arXiv preprint arXiv:1704.01444, 2017.Google ScholarGoogle Scholar
  27. S. Rendle. Factorization machines. In IEEE International Conference on Data Mining, ICDM 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. K. S. Tai, R. Socher, and C. D. Manning. Improved semantic representations from tree-structured long short-term memory networks. arXiv preprint arXiv:1503.00075, 2015.Google ScholarGoogle Scholar
  29. N. A. Vidya, M. I. Fanany, and I. Budi. Twitter sentiment to analyze net brand reputation of mobile phone providers. Procedia Computer Science, 72:519--526, 2015.Google ScholarGoogle ScholarCross RefCross Ref
  30. J. Zhao, G. Qiu, Z. Guan, W. Zhao, and X. He. Deep reinforcement learning for sponsored search real-time bidding. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Y. Zhou, S. Mishra, J. Gligorijevic, T. Bhatia, and N. Bhamidipati. Understanding consumer journey using attention based recurrent neural networks. KDD, 2019. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Z.-H. Zhou and J. Feng. Deep forest: Towards an alternative to deep neural networks. arXiv preprint arXiv:1702.08835, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Other conferences
      RecSys '19: Proceedings of the 13th ACM Conference on Recommender Systems
      September 2019
      635 pages
      ISBN:9781450362436
      DOI:10.1145/3298689

      Copyright © 2019 ACM

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      Publication History

      • Published: 10 September 2019

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      RecSys '19 Paper Acceptance Rate36of189submissions,19%Overall Acceptance Rate254of1,295submissions,20%

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