# Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions

arXiv: https://arxiv.org/abs/2512.01171 · HTML: https://mlsanity.com/paper/2512.01171/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 54 of its 116 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 30 of 54)

- [Temporal Convolutional Networks for Action Segmentation and Detection](https://mlsanity.com/paper/1611.05267/) (2016)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018)
- [ESCM2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation](https://mlsanity.com/paper/2204.05125/) (2022)
- [Loss Functions and Metrics in Deep Learning](https://mlsanity.com/paper/2307.02694/) (2023)
- [Large-scale Causal Approaches to Debiasing Post-click Conversion Rate Estimation with Multi-task Learning](https://mlsanity.com/paper/1910.09337/) (2019)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction](https://mlsanity.com/paper/2304.00902/) (2023)
- [Click-Through Rate Prediction in Online Advertising: A Literature Review](https://mlsanity.com/paper/2202.10462/) (2022)
- [Multilinear Mixture of Experts: Scalable Expert Specialization through Factorization](https://mlsanity.com/paper/2402.12550/) (2024)
- [ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction](https://mlsanity.com/paper/2310.09234/) (2023)
- [Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights](https://mlsanity.com/paper/2407.19467/) (2024)
- [Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising](https://mlsanity.com/paper/1907.10235/) (2019)
- [Real Negatives Matter: Continuous Training with Real Negatives for Delayed Feedback Modeling](https://mlsanity.com/paper/2104.14121/) (2021)
- [Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search](https://mlsanity.com/paper/1806.08211/) (2018)
- [Adversarial Multimodal Representation Learning for Click-Through Rate Prediction](https://mlsanity.com/paper/2003.07162/) (2020)
- [Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling](https://mlsanity.com/paper/2012.03245/) (2020)
- [A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction](https://mlsanity.com/paper/2211.06684/) (2022)
- [A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback](https://mlsanity.com/paper/2002.02068/) (2020)
- [User Response Prediction in Online Advertising](https://mlsanity.com/paper/2101.02342/) (2021)
- [Asymptotically Unbiased Estimation for Delayed Feedback Modeling via Label Correction](https://mlsanity.com/paper/2202.06472/) (2022)
- [UKD: Debiasing Conversion Rate Estimation via Uncertainty-regularized Knowledge Distillation](https://mlsanity.com/paper/2201.08024/) (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)
- [DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation](https://mlsanity.com/paper/2302.06141/) (2023)
- [Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles](https://mlsanity.com/paper/2001.11695/) (2020)
- [Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction](https://mlsanity.com/paper/2307.12756/) (2023)
- [Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising](https://mlsanity.com/paper/1808.03737/) (2018)
- [Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems](https://mlsanity.com/paper/2206.00407/) (2022)

## Abstract (arXiv)

Conversion and conversion rate (CVR) prediction play a critical role in efficient advertising decision-making. In past decades, although researchers have developed plenty of models for CVR prediction, the methodological evolution and relationships between different techniques have been precluded. In this paper, we conduct a comprehensive literature review on CVR prediction in online advertising, and classify state-of-the-art CVR prediction models into six categories with respect to the underlying techniques and elaborate on connections between these techniques. For each category of models, we present the framework of underlying techniques, their advantages and disadvantages, and discuss how they are utilized for CVR prediction. Moreover, we summarize the performance of various CVR prediction models on public and proprietary datasets. Finally, we identify research trends, major challenges, and promising future directions. We observe that results of performance evaluation reported in prior studies are not unanimous; semantics-enriched, attribution-enhanced, debiased CVR prediction and jointly modeling CTR and CVR prediction would be promising directions to explore in the future. This review is expected to provide valuable references and insights for future researchers and practitioners in this area.