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Commercial brand marketing Prediction based on deep learning and Image feature extraction
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Commercial brand marketing Prediction based on deep learning and Image feature extraction

by Pranay Goyal 1
1
Guru Gobind Singh Indraprastha University, School of Biotechnology, India
*
Author to whom correspondence should be addressed.
EMGBS  2023 2(1):1; https://doi.org/10.xxxx/xxxxxx
Received: 11 October 2023 / Accepted: 1 December 2023 / Published Online: 20 December 2023
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Abstract

With the development of Internet and E-commerce, picture advertising, as an important form of display advertising, has the characteristics of high visibility, strong readability and easy to obtain user recognition. An increasing number of Internet companies are paying attention to what kind of advertising pictures can attract more clicks. Recently, Deep Learning has been employed in automatic feature extraction, and has made remarkable achievements in the fields of computer vision, speech recognition, natural language processing and artificial intelligence. Compared with traditional shallow model, deep learning can automatically extract more complex features from simple features, which reduces the intervention of artificial feature engineering to a certain extent. Based on deep learning technology, this paper studies the prediction model of click through rate (CTR) for advertising, and proposes an end-to-end CTR prediction depth model for display advertising, which integrates the feature extraction of display advertising and CTR prediction to directly predict the probability of an advertisement image being clicked by users. This paper studies the deep-seated nonlinear characteristics through the multi-layer network structure of deep network, and carries out several groups of experiments on the private display advertising data set of a commercial advertising platform. The results show that the model proposed in this paper can effectively improve the prediction accuracy of CTR compared with other benchmark models.

Keywords: Click Through Rate; Multi-layer network; Display advertising;
Copyright: © 2023 by Goyal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Goyal, P. (2023). Commercial brand marketing Prediction based on deep learning and Image feature extraction. Economic Management & Global Business Studies, 2(1), 1. doi:10.xxxx/xxxxxx
ACS Style
Goyal, P. Commercial brand marketing Prediction based on deep learning and Image feature extraction. Economic Management & Global Business Studies, 2023, 2, 1. doi:10.xxxx/xxxxxx
AMA Style
Goyal P. Commercial brand marketing Prediction based on deep learning and Image feature extraction. Economic Management & Global Business Studies; 2023, 2(1):1. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Goyal, Pranay 2023. "Commercial brand marketing Prediction based on deep learning and Image feature extraction" Economic Management & Global Business Studies 2, no.1:1. doi:10.xxxx/xxxxxx
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ACS Style
Goyal, P. Commercial brand marketing Prediction based on deep learning and Image feature extraction. Economic Management & Global Business Studies, 2023, 2, 1. doi:10.xxxx/xxxxxx
AMA Style
Goyal P. Commercial brand marketing Prediction based on deep learning and Image feature extraction. Economic Management & Global Business Studies; 2023, 2(1):1. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Goyal, Pranay 2023. "Commercial brand marketing Prediction based on deep learning and Image feature extraction" Economic Management & Global Business Studies 2, no.1:1. doi:10.xxxx/xxxxxx
APA style
Goyal, P. (2023). Commercial brand marketing Prediction based on deep learning and Image feature extraction. Economic Management & Global Business Studies, 2(1), 1. doi:10.xxxx/xxxxxx

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References

  1. [1] Sanghavi M . MIDFLIGHT ONLINE ADVERTISEMENT CAMPAIGN OPTIMIZER: US, US20110112900 A1[P]. 2011.
  2. [2] Peteranetz M S , Flanigan A E , Shell D F , et al. Computational Creativity Exercises: An Avenue for Promoting Learning in Computer Science[J]. IEEE Transactions on Education, 2017, PP(4):1-9.
  3. [3] Ghose A , Yang S . An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets[J]. Social Science Electronic Publishing.
  4. [4] Broder A, Fontoura M, Josifovski V, et al. A semantic approach to contextual advertising[C]//Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 2007: 559-566.
  5. [5] Display Advertising[J]. International Journal of Gynecology & Obstetrics, 2003.
  6. [6] Chen J, Sun B, Li H, et al. Deep ctr prediction in display advertising[C]//Proceedings of the 2016 ACM on Multimedia Conference. ACM, 2016: 811-820.
  7. [7] Dalal N, Triggs B. Histograms of oriented gradients for human detection[C]//Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. IEEE, 2005, 1: 886-893
  8. [8] D . Lowe. Distinctive image features from scale-invariant key points[J]. International Journal of Computer Vision, 2003, 20:91-110.
  9. [9] Rao K , Sak H , Prabhavalkar R . Exploring Architectures, Data and Units For Streaming End-to-End Speech\n, Recognition with RNN-Transducer[C]// 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). IEEE, 2018.
  10. [10] Si J, Zhang H, Li C G, et al. Dual attention matching network for context-aware feature sequence based person re-identification[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 5363-5372.
  11. [11] Zongke Bao, Chenguang Wang, A multi-agent knowledge integration process for enterprise management innovation from the perspective of neural network, Information Processing & Management, Volume 59, Issue 2, 2022, 102873, ISSN 0306-4573.
  12. [12] Krizhevsky A , Sutskever I , Hinton G . ImageNet Classification with Deep Convolutional Neural Networks[J]. Advances in neural information processing systems, 2012, 25(2).
  13. [13] Azimi J, Zhang R, Zhou Y, et al. The impact of visual appearance on user response in online display advertising[C]//Proceedings of the 21st International Conference on World Wide Web. ACM, 2012: 457-458.
  14. [14] Cheng H, Zwol R, Azimi J, et al. Multimedia features for click prediction of new ads in display advertising[C]//Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012: 777-785.
  15. [15] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on pattern analysis and machine intelligence, 2002, 24(7): 971-987
  16. [16] Taha A , Chen Y T , Misu T , et al. In Defense of the Triplet Loss for Visual Recognition[J]. 2019.
  17. [17] Hughes L H , Schmitt M , Mou L , et al. Identifying Corresponding Patches in SAR and Optical Images with a Pseudo-Siamese CNN[J]. IEEE Geoscience and Remote Sensing Letters, 2018:1-5.
  18. [18] Mo K , Liu B , Xiao L , et al. Image feature learning for cold start problem in display advertising. 2015: 3728-3734.
  19. [19] Hosmer Jr D W, Lemeshow S, Sturdivant R X. Applied logistic regression[M]. John Wiley & Sons, 2013.
  20. [20] Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[J]. arXiv preprint arXiv:1502.03167, 2015.
  21. [21] Ge T , Zhao L , Zhou G , et al. Image Matters: Jointly Train Advertising CTR Model with Image Representation of Ad and User Behavior[J]. 2017.
  22. [22] Rendle S . Factorization Machines[C]// ICDM 2010, The 10th IEEE International Conference on Data Mining, Sydney, Australia, 14-17 December 2010. IEEE, 2010.
  23. [23] Juan Y , Zhuang Y , Chin W S , et al. Field-aware Factorization Machines for CTR Prediction[C]// the 10th ACM Conference. ACM, 2016.
  24. [24] Jahrer M, Toscher A, Lee J Y, et al. Ensemble of collaborative filtering and featureengineered models for click through rate prediction[C]//KDDCup Workshop. 2012.
  25. [25] Zhang W , Du T , Wang J . Deep Learning over Multi-field Categorical Data[C]//European Conference on Information Retrieval. Springer, Cham, 2016: 45-57.
  26. [26] Qu Y , Han C , Kan R , et al. Product-Based Neural Networks for User Response Prediction[C]// 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE, 2016: 1149-1154.
  27. [27] Xiao J , Ye H , He X , et al. Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks[J]. 2017.
  28. [28] Xu K , Ba J , Kiros R , et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention[J]. Computer Science, 2015:2048-2057.
  29. [29] Cheng H T , Koc L , Harmsen J , et al. Wide & Deep Learning for Recommender Systems[C]// Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, 2016.
  30. [30] Wang R , Fu B , Fu G , et al. Deep & Cross Network for Ad Click Predictions[C]// ADKDD'17. ACM, 2017.
  31. [31] Lehmann E L , Casella G . Theory of point estimation.[M]. Wiley, 1983.
  32. [32] Kingma D P, Ba J. Adam: A method for stochastic optimization[J]. arXiv preprint arXiv:1412.6980, 2014.
  33. [33] Seber G A F, Lee A J. Linear regression analysis[M]. John Wiley & Sons, 2012.