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

TL;DR

This paper introduces a dual-attention recurrent neural network that models user click sequences to improve multi-touch attribution in online advertising, outperforming existing methods and providing a principled evaluation scheme.

Contribution

It proposes a novel DARNN model that leverages attention mechanisms for better attribution of ad touch-points, integrating sequence modeling and combined attribution patterns.

Findings

Significant performance improvements over state-of-the-art models.

Effective utilization of user click sequences for attribution.

A practical evaluation scheme based on budget allocation.

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.