Abraham Bagherjeiran
ABSTRACT
In contextual advertising advertisers show ads to users so that they will click on them and eventually purchase a product. Optimizing this action sequence, called the conversion funnel, is the ultimate goal of advertising. Advertisers, however, often have very different sub-goals for their ads such as purchase, request for a quote, or simply a site visit. Often an improvement for one advertiser's goal comes at the expense of others. A single ranking function must balance these different goals in order to make an efficient system for all advertisers. We propose a ranking method that globally balances the goals of all advertisers, while simultaneously improving overall performance. Our method has been shown to improve significantly over the baseline in online traffic at a major ad network.
- Hila Becker, Andrei Broder, Evgeniy Gabrilovich, Vanja Josifovski, and Bo Pang. What happens after an ad click?: quantifying the impact of landing pages in web advertising. In CIKM, pages 57--66, 2009. Google Scholar
Digital Library
- Andrei Broder, Marcus Fontoura, Vanja Josifovski, and Lance Riedel. A semantic approach to contextual advertising. In SIGIR 2007, pages 559--566. ACM, 2007. Google ScholarDigital Library
- Deepayan Chakrabarti, Deepak Agarwal, and Vanja Josifovski. Contextual advertising by combining relevance with click feedback. In WWW 2008. ACM, 2008. Google ScholarDigital Library
- Benjamin Edelman, Michael Ostrovsky, and Michael Schwarz. Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. American Economic Review, 97, 2005.Google Scholar
- Anisio Lacerda, Marco Cristo, Marcos Goncalves, Fan Weiguo, Nivio Ziviani, and Berthier Ribeiro-Neto. Learning to advertise. In SIGIR 2006, pages 549--556. ACM, 2006. Google ScholarDigital Library
- Ling Li and Hsuan-Tien Lin. Ordinal regression by extended binary classification. In Advances in Neural Information Processing Systems 19, pages 865--872, 2007.Google Scholar
- Vanessa Murdock, Massimiliano Ciaramita, and Vassilis Plachouras. A noisy-channel approach to contextual advertising. In ADKDD 2007. ACM, 2007. Google ScholarDigital Library
- Adwait Ratnaparkhi. A hidden class page-ad probability model for contextual advertising. In Workshop on Targeting and Ranking for Online Advertising, at WWW 2008, Beijing, China, April 2008. Google ScholarDigital Library
- Berthier Ribeiro-Neto, Marco Cristo, Paulo Golgher, and Edleno Silva de Moura. Impedance coupling in content-targeted advertising. In SIGIR 2005. ACM, 2005. Google ScholarDigital Library
Index Terms
- Ranking for the conversion funnel
Recommendations
First workshop on targeting and ranking for online advertising
WWW '08: Proceedings of the 17th international conference on World Wide WebOnline advertising is a rapidly growing, multi-billion dollar industry. It has become a significant element of the Web browsing experience. Online advertising providers use sophisticated ad targeting and ranking algorithms with the dual aim of ...
A Framework to Harvest Page Views of Web for Banner Advertising
BDA 2015: Proceedings of the 4th International Conference on Big Data Analytics - Volume 9498Online advertising provides an opportunity for product sellers and service providers to reach customers and has become a key factor in the growth of economy. It is a major source of revenue for the major search engine and social networking sites. Search ...
Is Combining Contextual and Behavioral Targeting Strategies Effective in Online Advertising?
Online targeting has been increasingly used to deliver ads to consumers. But discovering how to target the most valuable web visitors and generate a high response rate is still a challenge for advertising intermediaries and advertisers. The purpose of ...
Comments