# Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model

arXiv: https://arxiv.org/abs/2405.05606 · HTML: https://mlsanity.com/paper/2405.05606/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 12 of 12)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [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)
- [Practice on Long Sequential User Behavior Modeling for Click-Through Rate Prediction](https://mlsanity.com/paper/1905.09248/) (2019)
- [Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings](https://mlsanity.com/paper/1904.11547/) (2019)
- [Learning to Warm Up Cold Item Embeddings for Cold-start Recommendation with Meta Scaling and Shifting Networks](https://mlsanity.com/paper/2105.04790/) (2021)
- [COLD: Towards the Next Generation of Pre-Ranking System](https://mlsanity.com/paper/2007.16122/) (2020)
- [Privileged Features Distillation at Taobao Recommendations](https://mlsanity.com/paper/1907.05171/) (2019)
- [Query Rewriting via Cycle-Consistent Translation for E-Commerce Search](https://mlsanity.com/paper/2103.00800/) (2021)
- [Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search](https://mlsanity.com/paper/2208.06150/) (2022)
- [COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://mlsanity.com/paper/2306.03516/) (2023)
- [Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search](https://mlsanity.com/paper/2303.11009/) (2023)

## Built on by (most cited first, 4 of 4)

- [AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping](https://mlsanity.com/paper/2602.12315/) (2026)
- [Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search](https://mlsanity.com/paper/2408.05751/) (2024)
- [MODRL-TA:A Multi-Objective Deep Reinforcement Learning Framework for Traffic Allocation in E-Commerce Search](https://mlsanity.com/paper/2407.15476/) (2024)
- [Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation](https://mlsanity.com/paper/2605.26424/) (2026)

## Abstract (arXiv)

In large e-commerce platforms, search systems are typically composed of a series of modules, including recall, pre-ranking, and ranking phases. The pre-ranking phase, serving as a lightweight module, is crucial for filtering out the bulk of products in advance for the downstream ranking module. Industrial efforts on optimizing the pre-ranking model have predominantly focused on enhancing ranking consistency, model structure, and generalization towards long-tail items. Beyond these optimizations, meeting the system performance requirements presents a significant challenge. Contrasting with existing industry works, we propose a novel method: a Generalizable and RAnk-ConsistEnt Pre-Ranking Model (GRACE), which achieves: 1) Ranking consistency by introducing multiple binary classification tasks that predict whether a product is within the top-k results as estimated by the ranking model, which facilitates the addition of learning objectives on common point-wise ranking models; 2) Generalizability through contrastive learning of representation for all products by pre-training on a subset of ranking product embeddings; 3) Ease of implementation in feature construction and online deployment. Our extensive experiments demonstrate significant improvements in both offline metrics and online A/B test: a 0.75% increase in AUC and a 1.28% increase in CVR.