# COLD: Towards the Next Generation of Pre-Ranking System

arXiv: https://arxiv.org/abs/2007.16122 · HTML: https://mlsanity.com/paper/2007.16122/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 30 of 31)

- [Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations](https://mlsanity.com/paper/2402.17152/) (2024)
- [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)
- [FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG](https://mlsanity.com/paper/2410.10293/) (2024)
- [Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach](https://mlsanity.com/paper/2105.07706/) (2021)
- [Sliding Spectrum Decomposition for Diversified Recommendation](https://mlsanity.com/paper/2107.05204/) (2021)
- [COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://mlsanity.com/paper/2306.03516/) (2023)
- [PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://mlsanity.com/paper/2302.03487/) (2023)
- [Cache-Aware Reinforcement Learning in Large-Scale Recommender Systems](https://mlsanity.com/paper/2404.14961/) (2024)
- [Decision-Making Context Interaction Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2301.12402/) (2023)
- [Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models](https://mlsanity.com/paper/2310.08039/) (2023)
- [Optimizing E-commerce Search: Toward a Generalizable and Rank-Consistent Pre-Ranking Model](https://mlsanity.com/paper/2405.05606/) (2024)
- [Truncation-Free Matching System for Display Advertising at Alibaba](https://mlsanity.com/paper/2102.09283/) (2021)
- [Computation Resource Allocation Solution in Recommender Systems](https://mlsanity.com/paper/2103.02259/) (2021)
- [Scaling Laws for Online Advertisement Retrieval](https://mlsanity.com/paper/2411.13322/) (2024)
- [A Hybrid Cross-Stage Coordination Pre-ranking Model for Online Recommendation Systems](https://mlsanity.com/paper/2502.10284/) (2025)
- [Unleashing the Potential of Two-Tower Models: Diffusion-Based Cross-Interaction for Large-Scale Matching](https://mlsanity.com/paper/2502.20687/) (2025)
- [InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction Features](https://mlsanity.com/paper/2504.06609/) (2025)
- [Spacetime-GR: A Spacetime-Aware Generative Model for Large Scale Online POI Recommendation](https://mlsanity.com/paper/2508.16126/) (2025)
- [AIF: Asynchronous Inference Framework for Cost-Effective Pre-Ranking](https://mlsanity.com/paper/2511.12934/) (2025)
- [RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems](https://mlsanity.com/paper/2409.13175/) (2024)
- [Residual Multi-Task Learner for Applied Ranking](https://mlsanity.com/paper/2411.09705/) (2024)
- [MTMD: A Multi-Task Multi-Domain Framework for Unified Ad Lightweight Ranking at Pinterest](https://mlsanity.com/paper/2510.09857/) (2025)
- [Hierarchical Multi-Interest Co-Network For Coarse-Grained Ranking](https://mlsanity.com/paper/2210.10547/) (2022)
- [Item Cold Start Recommendation via Adversarial Variational Auto-encoder Warm-up](https://mlsanity.com/paper/2302.14395/) (2023)
- [Graph Exploration Matters: Improving both individual-level and system-level diversity in WeChat Feed Recommender](https://mlsanity.com/paper/2306.00009/) (2023)
- [Methodologies for Improving Modern Industrial Recommender Systems](https://mlsanity.com/paper/2308.01204/) (2023)
- [An Efficient Embedding Based Ad Retrieval with GPU-Powered Feature Interaction](https://mlsanity.com/paper/2511.22460/) (2025)
- [FairFS: Addressing Deep Feature Selection Biases for Recommender System](https://mlsanity.com/paper/2602.20001/) (2026)
- [Not All Candidates are Created Equal: A Heterogeneity-Aware Approach to Pre-ranking in Recommender Systems](https://mlsanity.com/paper/2603.03770/) (2026)

## Abstract (arXiv)

Multi-stage cascade architecture exists widely in many industrial systems such as recommender systems and online advertising, which often consists of sequential modules including matching, pre-ranking, ranking, etc. For a long time, it is believed pre-ranking is just a simplified version of the ranking module, considering the larger size of the candidate set to be ranked. Thus, efforts are made mostly on simplifying ranking model to handle the explosion of computing power for online inference. In this paper, we rethink the challenge of the pre-ranking system from an algorithm-system co-design view. Instead of saving computing power with restriction of model architecture which causes loss of model performance, here we design a new pre-ranking system by joint optimization of both the pre-ranking model and the computing power it costs. We name it COLD (Computing power cost-aware Online and Lightweight Deep pre-ranking system). COLD beats SOTA in three folds: (i) an arbitrary deep model with cross features can be applied in COLD under a constraint of controllable computing power cost. (ii) computing power cost is explicitly reduced by applying optimization tricks for inference acceleration. This further brings space for COLD to apply more complex deep models to reach better performance. (iii) COLD model works in an online learning and severing manner, bringing it excellent ability to handle the challenge of the data distribution shift. Meanwhile, the fully online pre-ranking system of COLD provides us with a flexible infrastructure that supports efficient new model developing and online A/B testing.Since 2019, COLD has been deployed in almost all products involving the pre-ranking module in the display advertising system in Alibaba, bringing significant improvements.