# Click-Conversion Multi-Task Model with Position Bias Mitigation for Sponsored Search in eCommerce

arXiv: https://arxiv.org/abs/2307.16060 · HTML: https://mlsanity.com/paper/2307.16060/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 29 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Unbiased Learning to Rank with Unbiased Propensity Estimation](https://mlsanity.com/paper/1804.05938/) (2018)
- [Modeling the Sequential Dependence among Audience Multi-step Conversions with Multi-task Learning in Targeted Display Advertising](https://mlsanity.com/paper/2105.08489/) (2021)
- [Estimating Position Bias without Intrusive Interventions](https://mlsanity.com/paper/1812.05161/) (2018)
- [Cascade Model-based Propensity Estimation for Counterfactual Learning to Rank](https://mlsanity.com/paper/2005.11938/) (2020)
- [A Study of Position Bias in Digital Library Recommender Systems](https://mlsanity.com/paper/1802.06565/) (2018)
- [Deep Position-wise Interaction Network for CTR Prediction](https://mlsanity.com/paper/2106.05482/) (2021)
- [A Deep Recurrent Survival Model for Unbiased Ranking](https://mlsanity.com/paper/2004.14714/) (2020)

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

- [Localize, Retrieve and Fuse: A Generalized Framework for Free-Form Question Answering over Tables](https://mlsanity.com/paper/2309.11049/) (2023)
- [Practical Multi-Task Learning for Rare Conversions in Ad Tech](https://mlsanity.com/paper/2507.20161/) (2025)
- [Enhancement of E-commerce Sponsored Search Relevancy with LLM](https://mlsanity.com/paper/2607.03886/) (2026)
- [Information Gain-Guided Causal Intervention for Autonomous Debiasing Large Language Models](https://mlsanity.com/paper/2504.12898/) (2025)
- [OptAgent: Optimizing Query Rewriting for E-commerce via Multi-Agent Simulation](https://mlsanity.com/paper/2510.03771/) (2025)

## Abstract (arXiv)

Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems. Position bias in training data biases the ranking model, leading to increasingly unfair item rankings, click-through-rate (CTR), and conversion rate (CVR) predictions. To jointly mitigate position bias in both item CTR and CVR prediction, we propose two position-bias-free CTR and CVR prediction models: Position-Aware Click-Conversion (PACC) and PACC via Position Embedding (PACC-PE). PACC is built upon probability decomposition and models position information as a probability. PACC-PE utilizes neural networks to model product-specific position information as embedding. Experiments on the E-commerce sponsored product search dataset show that our proposed models have better ranking effectiveness and can greatly alleviate position bias in both CTR and CVR prediction.