# See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction

arXiv: https://arxiv.org/abs/2508.15217 · HTML: https://mlsanity.com/paper/2508.15217/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 30 of 32)

- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [Real-Time Bidding by Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1701.02490/) (2017)
- [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021)
- [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://mlsanity.com/paper/1802.09756/) (2018)
- [Optimized Cost per Click in Taobao Display Advertising](https://mlsanity.com/paper/1703.02091/) (2017)
- [ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://mlsanity.com/paper/2204.05125/) (2022)
- [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://mlsanity.com/paper/1610.03013/) (2016)
- [Bid Optimization by Multivariable Control in Display Advertising](https://mlsanity.com/paper/1905.10928/) (2019)
- [Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models](https://mlsanity.com/paper/2209.06053/) (2022)
- [Neural Auction: End-to-End Learning of Auction Mechanisms for E-Commerce Advertising](https://mlsanity.com/paper/2106.03593/) (2021)
- [Sustainable Online Reinforcement Learning for Auto-bidding](https://mlsanity.com/paper/2210.07006/) (2022)
- [Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights](https://mlsanity.com/paper/2407.19467/) (2024)
- [Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model](https://mlsanity.com/paper/2208.06164/) (2022)
- [Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction](https://mlsanity.com/paper/2104.09713/) (2021)
- [Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling](https://mlsanity.com/paper/2104.14121/) (2021)
- [Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling](https://mlsanity.com/paper/2012.03245/) (2020)
- [HoME: Hierarchy of Multi-Gate Experts for Multi-Task Learning at Kuaishou](https://mlsanity.com/paper/2408.05430/) (2024)
- [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](https://mlsanity.com/paper/2202.06472/) (2022)
- [Deep Neural Net with Attention for Multi-channel Multi-touch Attribution](https://mlsanity.com/paper/1809.02230/) (2018)
- [CAMTA: Causal Attention Model for Multi-touch Attribution](https://mlsanity.com/paper/2012.11403/) (2020)
- [CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution](https://mlsanity.com/paper/2201.00689/) (2021)
- [KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](https://mlsanity.com/paper/2208.10174/) (2022)
- [AdaTT: Adaptive Task-to-Task Fusion Network for Multitask Learning in Recommendations](https://mlsanity.com/paper/2304.04959/) (2023)
- [Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach](https://mlsanity.com/paper/2305.12837/) (2023)
- [Hierarchically Constrained Adaptive Ad Exposure in Feeds](https://mlsanity.com/paper/2205.15759/) (2022)
- [Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction](https://mlsanity.com/paper/2308.04768/) (2023)
- [On Designing a Two-stage Auction for Online Advertising](https://mlsanity.com/paper/2111.05555/) (2021)
- [Contextual Generative Auction with Permutation-level Externalities for Online Advertising](https://mlsanity.com/paper/2412.11544/) (2024)

## Built on by (most cited first, 5 of 5)

- [Modeling Cascaded Delay Feedback for Online Net Conversion Rate Prediction: Benchmark, Insights and Solutions](https://mlsanity.com/paper/2601.19965/) (2026)
- [Delayed Feedback Modeling for Post-Click Gross Merchandise Volume Prediction: Benchmark, Insights and Approaches](https://mlsanity.com/paper/2601.20307/) (2026)
- [MAC: A Conversion Rate Prediction Benchmark Featuring Labels Under Multiple Attribution Mechanisms](https://mlsanity.com/paper/2603.02184/) (2026)
- [Statistical Learning from Attribution Sets](https://mlsanity.com/paper/2602.06276/) (2026)
- [Can Large Language Models Identify Meaningful Touchpoints in Conversion Attribution?](https://mlsanity.com/paper/2608.28649/) (2026)

## Abstract (arXiv)

Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-fundamentally determine label generation and model optimization. While many industrial platforms support diverse attribution mechanisms (e.g., First-Click, Last-Click, Linear, and Data-Driven Multi-Touch Attribution), conventional approaches restrict model training to labels from a single production-critical attribution mechanism, discarding complementary signals in alternative attribution perspectives. To address this limitation, we propose a novel Multi-Attribution Learning (MAL) framework for CVR prediction that integrates signals from multiple attribution perspectives to better capture the underlying patterns driving user conversions. Specifically, MAL is a joint learning framework consisting of two core components: the Attribution Knowledge Aggregator (AKA) and the Primary Target Predictor (PTP). AKA is implemented as a multi-task learner that integrates knowledge extracted from diverse attribution labels. PTP, in contrast, focuses on the task of generating well-calibrated conversion probabilities that align with the system-optimized attribution metric (e.g., CVR under the Last-Click attribution), ensuring direct compatibility with industrial deployment requirements. Additionally, we propose CAT, a novel training strategy that leverages the Cartesian product of all attribution label combinations to generate enriched supervision signals. This design substantially enhances the performance of the attribution knowledge aggregator. Empirical evaluations demonstrate the superiority of MAL over single-attribution learning baselines, achieving +0.51% GAUC improvement on offline metrics. Online experiments demonstrate that MAL achieved a +2.6% increase in ROI (Return on Investment).