publication . Preprint . Conference object . 2018

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

Kan Ren; Yuchen Fang; Weinan Zhang; Shuhao Liu; Jiajun Li; Ya Zhang; Yong Yu; Jun Wang;
Open Access English
  • Published: 10 Aug 2018
  • Country: United Kingdom
Abstract
In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users' pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patter...
Subjects
free text keywords: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Conversion Attribution, Multi-Touch Attribution, Computational Advertising, Attention Mechanism, Deep Learning, Computer science, Information retrieval, Recurrent neural network, Online advertising, business.industry, business, Artificial intelligence, The Internet, Multi-touch, Attribution, Database transaction, Advertising campaign, Deep learning
Related Organizations
37 references, page 1 of 3

[1] Deepak Agarwal, Souvik Ghosh, Kai Wei, and Siyu You. 2014. Budget pacing for targeted online advertisements at LinkedIn. In KDD. ACM, 1613-1619. [OpenAIRE]

[2] Kareem Amin, Michael Kearns, Peter Key, and Anton Schwaighofer. 2012. Budget optimization for sponsored search: Censored learning in MDPs. UAI (2012).

[3] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

[4] Ron Berman. 2017. Beyond the last touch: Attribution in online advertising. (2017).

[5] Xuezhi Cao, Haokun Chen, Xuejian Wang, Weinan Zhang, and Yong Yu. 2018. Neural Link Prediction over Aligned Networks. In AAAI.

[6] John Chandler-Pepelnjak. 2009. Measuring roi beyond the last ad. Atlas Institute Digital Marketing Insight (2009), 1-6.

[7] Brian Dalessandro, Claudia Perlich, Ori Stitelman, and Foster Provost. 2012. Causally motivated attribution for online advertising. In ADKDD. ACM, 7.

[8] Eustache Diemert, Julien Meynet, Pierre Galland, and Damien Lefortier. 2017. Attribution Modeling Increases Eficiency of Bidding in Display Advertising. In ADKDD. ACM.

[9] Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122 (2017).

[10] Sahin Cem Geyik, Abhishek Saxena, and Ali Dasdan. 2014. Multi-touch attribution based budget allocation in online advertising. In ADKDD. ACM, 1-9.

[11] Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation (1997).

[12] Wendi Ji and Xiaoling Wang. 2017. Additional Multi-Touch Attribution for Online Advertising. In AAAI.

[13] Wendi Ji, Xiaoling Wang, and Dell Zhang. 2016. A probabilistic multi-touch attribution model for online advertising. In CIKM. ACM. [OpenAIRE]

[14] Kuang-Chih Lee, Ali Jalali, and Ali Dasdan. 2013. Real time bid optimization with smooth budget delivery in online advertising. In ADKDD. ACM, 1.

[15] Kuang-chih Lee, Burkay Orten, Ali Dasdan, and Wentong Li. 2012. Estimating conversion rate in display advertising from past performance data. In KDD. ACM, 768-776. [OpenAIRE]

37 references, page 1 of 3
Abstract
In online advertising, the Internet users may be exposed to a sequence of different ad campaigns, i.e., display ads, search, or referrals from multiple channels, before led up to any final sales conversion and transaction. For both campaigners and publishers, it is fundamentally critical to estimate the contribution from ad campaign touch-points during the customer journey (conversion funnel) and assign the right credit to the right ad exposure accordingly. However, the existing research on the multi-touch attribution problem lacks a principled way of utilizing the users' pre-conversion actions (i.e., clicks), and quite often fails to model the sequential patter...
Subjects
free text keywords: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Conversion Attribution, Multi-Touch Attribution, Computational Advertising, Attention Mechanism, Deep Learning, Computer science, Information retrieval, Recurrent neural network, Online advertising, business.industry, business, Artificial intelligence, The Internet, Multi-touch, Attribution, Database transaction, Advertising campaign, Deep learning
Related Organizations
37 references, page 1 of 3

[1] Deepak Agarwal, Souvik Ghosh, Kai Wei, and Siyu You. 2014. Budget pacing for targeted online advertisements at LinkedIn. In KDD. ACM, 1613-1619. [OpenAIRE]

[2] Kareem Amin, Michael Kearns, Peter Key, and Anton Schwaighofer. 2012. Budget optimization for sponsored search: Censored learning in MDPs. UAI (2012).

[3] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014).

[4] Ron Berman. 2017. Beyond the last touch: Attribution in online advertising. (2017).

[5] Xuezhi Cao, Haokun Chen, Xuejian Wang, Weinan Zhang, and Yong Yu. 2018. Neural Link Prediction over Aligned Networks. In AAAI.

[6] John Chandler-Pepelnjak. 2009. Measuring roi beyond the last ad. Atlas Institute Digital Marketing Insight (2009), 1-6.

[7] Brian Dalessandro, Claudia Perlich, Ori Stitelman, and Foster Provost. 2012. Causally motivated attribution for online advertising. In ADKDD. ACM, 7.

[8] Eustache Diemert, Julien Meynet, Pierre Galland, and Damien Lefortier. 2017. Attribution Modeling Increases Eficiency of Bidding in Display Advertising. In ADKDD. ACM.

[9] Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N Dauphin. 2017. Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122 (2017).

[10] Sahin Cem Geyik, Abhishek Saxena, and Ali Dasdan. 2014. Multi-touch attribution based budget allocation in online advertising. In ADKDD. ACM, 1-9.

[11] Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation (1997).

[12] Wendi Ji and Xiaoling Wang. 2017. Additional Multi-Touch Attribution for Online Advertising. In AAAI.

[13] Wendi Ji, Xiaoling Wang, and Dell Zhang. 2016. A probabilistic multi-touch attribution model for online advertising. In CIKM. ACM. [OpenAIRE]

[14] Kuang-Chih Lee, Ali Jalali, and Ali Dasdan. 2013. Real time bid optimization with smooth budget delivery in online advertising. In ADKDD. ACM, 1.

[15] Kuang-chih Lee, Burkay Orten, Ali Dasdan, and Wentong Li. 2012. Estimating conversion rate in display advertising from past performance data. In KDD. ACM, 768-776. [OpenAIRE]

37 references, page 1 of 3
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publication . Preprint . Conference object . 2018

Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising

Kan Ren; Yuchen Fang; Weinan Zhang; Shuhao Liu; Jiajun Li; Ya Zhang; Yong Yu; Jun Wang;