Ji, W. and Wang, X. and Zhang, Dell (2016) A probabilistic multi-touch attribution model for online advertising. In: UNSPECIFIED (ed.) Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM). New York, U.S.: Association for Computing Machinery, pp. 1373-1382. ISBN 9781450340731.
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Abstract
It is an important problem in computational advertising to study the effects of different advertising channels upon user conversions, as advertisers can use the discoveries to plan or optimize advertising campaigns. In this paper, we propose a novel Probabilistic Multi-Touch Attribution (PMTA) model which takes into account not only which ads have been viewed or clicked by the user but also when each such interaction occurred. Borrowing the techniques from survival analysis, we use the Weibull distribution to describe the observed conversion delay and use the hazard rate of conversion to measure the influence of an ad exposure. It has been shown by extensive experiments on a large real-world dataset that our proposed model is superior to state-of-the-art methods in both conversion prediction and attribution analysis. Furthermore, a surprising research finding obtained from this dataset is that search ads are often not the root cause of final conversions but just the consequence of previously viewed ads.
Metadata
| Item Type: | Book Section |
|---|---|
| Additional Information: | (c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Keyword(s) / Subject(s): | Computational Advertising, Multi-Touch Attribution, Survival Analysis |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab, Data Analytics, Birkbeck Institute for |
| Depositing User: | Dell Zhang |
| Date Deposited: | 24 Nov 2016 12:09 |
| Last Modified: | 09 Aug 2023 12:38 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/16166 |
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