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

arXiv: https://arxiv.org/abs/1808.03737 · HTML: https://mlsanity.com/paper/1808.03737/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution](https://mlsanity.com/paper/2201.00689/) (2021)
- [Generative Auto-Bidding in Large-Scale Competitive Auctions via Diffusion Completer-Aligner](https://mlsanity.com/paper/2509.03348/) (2025)
- [FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications](https://mlsanity.com/paper/2511.14865/) (2025)
- [BAT: Benchmark for Auto-bidding Task](https://mlsanity.com/paper/2505.08485/) (2025)
- [Multi-task Offline Reinforcement Learning for Online Advertising in Recommender Systems](https://mlsanity.com/paper/2506.23090/) (2025)
- [DCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution](https://mlsanity.com/paper/2401.08875/) (2024)
- [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025)
- [Causal-driven attribution (CDA): Estimating channel influence without user-level data](https://mlsanity.com/paper/2512.21211/) (2025)
- [Sequential Multimodal Evidence Optimization for Product Media Ranking in E-Commerce](https://mlsanity.com/paper/2608.15662/) (2026)

## Abstract (arXiv)

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.