Abstract
We present a new algorithm for behavioral targeting of banner advertisements. We record different user’s actions such as clicks, search queries and pageviews. We use the collected information to estimate in real time the probability of a click on a banner. Each click on a banner generates a profit. Our goal is to maximize the overall profit. We use a naive Bayesian model. We keep track of the click frequencies of the different banners under the additional information provided by the actions that each user has performed. We apply our strategy on real data in which we simply use the hours during which a user is connected as a feature. We describe the results obtained on these real data that give support to the effectiveness of our strategy. Moreover we describe some heuristics to improve the estimate of the click frequencies and to avoid displaying the same banner to the same user too many times.
Similar content being viewed by others
Notes
The distance between two hours h 1 and h 2 is computed as follows. Without loss of generality, let h 1 ≤ h 2. Then their distance is min(h 2 − h 1, 24 + h 1 − h 2).
References
Abe N, Nakamura A (1999) Learning to optimally schedule internet banner advertisements. In ICML’99: Proceedings of the sixteenth international conference on machine learning, pp. 12–21
Caruso F, Giuffrida G (2012) Optimized delivery of on-Line advertisements. In: Proceedings of ICAART 2012
Caruso F, Giuffrida G, Zarba C (2011) Real-time behavioral targeting of banner advertising. In: Proceedings of CLADAG 2012
Chen Y, Pavlov D, Canny JF (2009) Large-scale behavioral targeting. In KDD ’09: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp. 209–218
Jun Y, Ning L, Gang W, Wen Z, Yun J, Zheng C (2009) How much can behavioral targeting help online advertising? In: 18th international world wide web conference (WWW2009)
Nakamura A, Abe N (2005) Improvements to the linear programming based scheduling of web advertisements. Electron Commer Res 5(1):75–98
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Caruso, F., Giuffrida, G. & Zarba, C. Heuristic Bayesian targeting of banner advertising. Optim Eng 16, 247–257 (2015). https://doi.org/10.1007/s11081-014-9248-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11081-014-9248-8