# Capturing Conversion Rate Fluctuation during Sales Promotions: A Novel Historical Data Reuse Approach arXiv: https://arxiv.org/abs/2305.12837 ยท HTML: https://mlsanity.com/paper/2305.12837/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 42 references that are in the map. - First posted on arXiv 22 May 2023. ## Builds on (most cited first, 26 of 26) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017) - [Efficient Test-Time Model Adaptation without Forgetting](https://mlsanity.com/paper/2204.02610/) (2022) - [Detecting and Correcting for Label Shift with Black Box Predictors](https://mlsanity.com/paper/1802.03916/) (2018) - [Contrastive Test-Time Adaptation](https://mlsanity.com/paper/2204.10377/) (2022) - [Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://mlsanity.com/paper/1804.07931/) (2018) - [Regularized Learning for Domain Adaptation under Label Shifts](https://mlsanity.com/paper/1903.09734/) (2019) - [One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction](https://mlsanity.com/paper/2101.11427/) (2021) - [Improving Test-Time Adaptation via Shift-agnostic Weight Regularization and Nearest Source Prototypes](https://mlsanity.com/paper/2207.11707/) (2022) - [Open Benchmarking for Click-Through Rate Prediction](https://mlsanity.com/paper/2009.05794/) (2020) - [Online Adaptation to Label Distribution Shift](https://mlsanity.com/paper/2107.04520/) (2021) - [Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models](https://mlsanity.com/paper/2209.06053/) (2022) - [Adapting to Online Label Shift with Provable Guarantees](https://mlsanity.com/paper/2207.02121/) (2022) - [Addressing Delayed Feedback for Continuous Training with Neural Networks in CTR prediction](https://mlsanity.com/paper/1907.06558/) (2019) - [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) - [PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems](https://mlsanity.com/paper/2204.04903/) (2022) - [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) - [Delayed Feedback Modeling for the Entire Space Conversion Rate Prediction](https://mlsanity.com/paper/2011.11826/) (2020) - [MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty Calibration](https://mlsanity.com/paper/2202.04348/) (2022) - [KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging](https://mlsanity.com/paper/2208.10174/) (2022) - [Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems](https://mlsanity.com/paper/2206.00407/) (2022) - [Adversarial Gradient Driven Exploration for Deep Click-Through Rate Prediction](https://mlsanity.com/paper/2112.11136/) (2021) ## Built on by (most cited first, 8 of 8) - [Enhancing Taobao Display Advertising with Multimodal Representations: Challenges, Approaches and Insights](https://mlsanity.com/paper/2407.19467/) (2024) - [COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://mlsanity.com/paper/2306.03516/) (2023) - [See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction](https://mlsanity.com/paper/2508.15217/) (2025) - [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) - [Conversion rate prediction in online advertising: modeling techniques, performance evaluation and future directions](https://mlsanity.com/paper/2512.01171/) (2025) - [JD-BP: A Joint-Decision Generative Framework for Auto-Bidding and Pricing](https://mlsanity.com/paper/2604.05845/) (2026) ## Abstract (arXiv) Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions. This can be largely ascribed to the problem of the data distribution shift, in which the conventional methods no longer work. To this end, we seek to develop alternative modeling techniques for CVR prediction. Observing similar purchase patterns across different promotions, we propose reusing the historical promotion data to capture the promotional conversion patterns. Herein, we propose a novel Historical Data Reuse (HDR) approach that first retrieves historically similar promotion data and then fine-tunes the CVR prediction model with the acquired data for better adaptation to the promotion mode. HDR consists of three components: an automated data retrieval module that seeks similar data from historical promotions, a distribution shift correction module that re-weights the retrieved data for better aligning with the target promotion, and a TransBlock module that quickly fine-tunes the original model for better adaptation to the promotion mode. Experiments conducted with real-world data demonstrate the effectiveness of HDR, as it improves both ranking and calibration metrics to a large extent. HDR has also been deployed on the display advertising system in Alibaba, bringing a lift of 9% RPM and 16% CVR during Double 11 Sales in 2022.