publication . Conference object . 2016

a probabilistic multi touch attribution model for online advertising

Wendi Ji; Xiaoling Wang; Dell Zhang;
Open Access
  • Published: 24 Oct 2016
  • Publisher: ACM
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 exp...
Subjects
free text keywords: Advertising campaign, Machine learning, computer.software_genre, computer, Search advertising, Root cause, Data mining, Artificial intelligence, business.industry, business, Probabilistic logic, Computer science, Attribution, Online advertising, Weibull distribution, Communication channel
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