Article

Topic-based user segmentation for online advertising with latent dirichlet allocation

Authors:

Chaojun LuAuthors Info & Claims

ADMA'10: Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II

Pages 259 - 269

Published: 19 November 2010 Publication History

Abstract

Behavioral Targeting (BT), as a useful technique to deliver the most appropriate advertisements to the most interested users by analyzing the user behaviors pattern, has gained considerable attention in online advertising market in recent year. A main task of BT is how to automatically segment web users for ads delivery, and good user segmentation may greatly improve the effectiveness of their campaigns and increase the ad click-through rate (CTR). Classical user segmentation methods, however, rarely take the semantics of user behaviors into consideration and can not mine the user behavioral pattern as properly as should be expected. In this paper, we propose an innovative approach based on the effective semantic analysis algorithm Latent Dirichlet Allocation (LDA) to attack this problem. Comparisons with other three baseline algorithms through experiments have confirmed that the proposed approach can increase effectiveness of user segmentation significantly.

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Cited By

Qin RYuan YWang F(2017)Exploring the optimal granularity for market segmentation in RTB advertising via computational experiment approachElectronic Commerce Research and Applications10.1016/j.elerap.2017.07.00124:C(68-83)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.elerap.2017.07.001
Saia RBoratto LCarta SFenu G(2016)Binary sievesFuture Generation Computer Systems10.1016/j.future.2016.04.00664:C(186-197)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.future.2016.04.006
Shen JGeyik SDasdan ACao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Effective Audience Extension in Online AdvertisingProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2788603(2099-2108)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2788603
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Information & Contributors

Information

Published In

cover image Guide Proceedings

ADMA'10: Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II

November 2010

567 pages

ISBN:3642173128

Editors:
Longbing Cao
University of Technology Sydney, Faculty of Engineering and Information Technology, Sydney, NSW, Australia
,
Yong Feng
Chongqing University, College of Computer Science, Chongqing, China
,
Jiang Zhong
Chongqing University, College of Computer Science, Chongqing, China

Sponsors

NSF of China: National Natural Science Foundation of China
Chongqing Sci. and Technol. Comm.: Chongqing Science and Technology Commission
Chongqing Acad. Sci. Technol.: Chongqing Academy of Science and Technology
IEEE Queensland Sec.: IEEE Queensland Sec.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 19 November 2010

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Cited By

Qin RYuan YWang F(2017)Exploring the optimal granularity for market segmentation in RTB advertising via computational experiment approachElectronic Commerce Research and Applications10.1016/j.elerap.2017.07.00124:C(68-83)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.elerap.2017.07.001
Saia RBoratto LCarta SFenu G(2016)Binary sievesFuture Generation Computer Systems10.1016/j.future.2016.04.00664:C(186-197)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.future.2016.04.006
Shen JGeyik SDasdan ACao LZhang CJoachims TWebb GMargineantu DWilliams G(2015)Effective Audience Extension in Online AdvertisingProceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2783258.2788603(2099-2108)Online publication date: 10-Aug-2015
https://dl.acm.org/doi/10.1145/2783258.2788603
Pepelyshev AStaroselskiy YZhigljavsky A(2015)Adaptive Targeting for Online AdvertisementRevised Selected Papers of the First International Workshop on Machine Learning, Optimization, and Big Data - Volume 943210.1007/978-3-319-27926-8_21(240-251)Online publication date: 21-Jul-2015
https://dl.acm.org/doi/10.1007/978-3-319-27926-8_21

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