Machine Learning Optimization in Computational Advertising—A Systematic Literature Review-Reference-Cited by-同舟云学术

Machine Learning Optimization in Computational Advertising—A Systematic Literature Review

Published:2022 Issue: Volume: Page:97-111
ISSN:2198-4182
Container-title:Studies in Systems, Decision and Control
language:
Short-container-title:

Author:

Truong Vinh,Hoang Vinh

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-04028-3_8

Reference34 articles.

1. Lemon, K.N., Verhoef, P.C.: Understanding customer experience throughout the customer journey. J. Mark. 80(6), 69–96 (2016)

2. Grewal, D., Bart, Y., Spann, M., Zubcsek, P.P.: Mobile advertising: A framework and research agenda. J. Interact. Mark. 34(June), 3–14 (2016). https://doi.org/10.1016/j.intmar.2016.03.003

3. Ghosh, A., Mitra, S., Sarkhel, S., Xie, J., Wu, G., Swaminathan, V.: Scalable Bid Landscape Forecasting in Real-Time Bidding, vol. 11908 LNAI (2020). https://doi.org/10.1007/978-3-030-46133-1_27

4. Choi, H., Mela, C., Balseiro, S., Leary, A.: Online display advertising markets: A literature review and future directions. Columbia Business School Research Paper 18-1 (2017) [Online]. Available: https://ssrn.com/abstract=3070706

5. Kaput, M.: Artificial Intelligence in Advertising (2021) [Online]. Available: https://www.marketingaiinstitute.com/blog/ai-in-advertising

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