Abstract
The RTB system is a bidding system for advertising in a specific area of on-line page. A typical RTB bidding system is a system provided by Google’s search engine. In this paper, we use the data of the Naver advertisement bidding, a representative Korean search engine operated by a private bidding for the RTB system. Especially, in case of online keyword advertisement, the rank can be important factor the online page when a user enters a certain keyword into a search engine. For example, if a search keyword is ranked at the top of an online page, the probability of bid being directly connected will be increased for the link of related keyword. Therefore, the bid price of the keyword is changed according to the rank of the search keyword. In the end, it is necessary to find an appropriate bid price for registering a keyword in a private bid system. In this paper, we propose a prediction modeling mechanism to predict optimal bid price of the keyword in a specific ranking of search engine. In order to predict the optimal bid price and advertising ranking on the online page, we perform feature engineering on the related data set and define the prediction model using the machine learning algorithms for the corresponding data set.
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References
Hou, L.: A hierarchical bayesian network-based approach to keyword auction. IEEE Trans. Eng. Manag. 62, 217–225 (2015)
Lauritzen, S.: The EM algorithm for graphical association models with missing data. Comput. Stat. Data Anal. 19, 191–201 (1995)
Shuai, Y., Wang, J., Zhao, X.: Real-time bidding for online advertising: measurement and analysis. In: International Workshop on Data Mining for Online Advertising, Chicago, Illinois, USA (2013)
Brooks, N.: The Atlas rank report: How search engine rank impacts conversions (2004). http://www.atlasonepoint.com/pdf/AtlasRankReportPart2.pdf
Auerbach, J., Galenson, J., Sundararajan, M.: An empirical analysis of return on investment maximization in sponsored search auctions. In: International Workshop Data Mining and Audience Intelligence, Las Vegas, NV, USA (2008)
Stepanchuk, T.: An empirical examination of the relation between bids and positions of ads in sponsored search. In: 21st Bled eConference on eCollaboration: Overcoming Boundaries through Multichannel Interaction. Bled, Slovenia (2008)
Jerath, K., Ma, L., Park, Y., Srinivasan, K.: A position paradox in sponsored search auctions. Mark. Sci. 30, 612–627 (2011)
Gopal, R., Li, X., Sankaranarayanan, R.: Online keyword based advertising impact of ad impressions on own channel and cross channel click through rates. Decis. Support Syst. 52, 1–8 (2011)
Graepel, T., Candela, J., Borchert, T., Herbrich, R.: Web-scale Bayesian click through rate prediction for sponsored search advertising in Microsoft Bing search engine. In: International Conference on Machine Learning, Haifa, Israel (2010)
Kingma, D., Ba, J.: ADAM: a method for stochastic optimization. In: International Conference on Learning Representations, San Diego, CA, USA (2015)
Acknowledgements
This work was supported by the Technology development Program (C0563763) funded by the Ministry of SMEs and Startups (MSS, Korea). The keyword data set for the simulation in this paper was supported by Taeseong Kim of the e-Glue communications.
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Ji, M., Park, H. (2019). Prediction Model of Optimal Bid Price Based on Keyword Auction Data Through Machine Learning Algorithms. In: Barolli, L., Xhafa, F., Javaid, N., Enokido, T. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2018. Advances in Intelligent Systems and Computing, vol 773. Springer, Cham. https://doi.org/10.1007/978-3-319-93554-6_65
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DOI: https://doi.org/10.1007/978-3-319-93554-6_65
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