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Learning multi-touch conversion attribution with dual-attention mechanisms for online advertising

Yu, Y; Wang, J; (2018) Learning multi-touch conversion attribution with dual-attention mechanisms for online advertising. In: (Proceedings) Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM '18). (pp. pp. 1433-1442). ACM: Torino, Italy. Green open access

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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 patterns among the touch points from a user's behavior data. To make it worse, the current industry practice is merely employing a set of arbitrary rules as the attribution model, e.g., the popular last-touch model assigns 100% credit to the final touch-point regardless of actual attributions. In this paper, we propose a Dual-attention Recurrent Neural Network (DARNN) for the multi-touch attribution problem. It learns the attribution values through an attention mechanism directly from the conversion estimation objective. To achieve this, we utilize sequence-to-sequence prediction for user clicks, and combine both post-view and post-click attribution patterns together for the final conversion estimation. To quantitatively benchmark attribution models, we also propose a novel yet practical attribution evaluation scheme through the proxy of budget allocation (under the estimated attributions) over ad channels. The experimental results on two real datasets demonstrate the significant performance gains of our attribution model against the state of the art.

Type: Proceedings paper
Title: Learning multi-touch conversion attribution with dual-attention mechanisms for online advertising
Event: Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM '18)
ISBN-13: 9781450360142
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3269206.3271677
Publisher version: https://doi.org/10.1145/3269206.3271677
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Conversion Attribution, Multi-Touch Attribution, Computational Advertising, Attention Mechanism, Deep Learning
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10066098
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