# Towards a Better Tradeoff between Effectiveness and Efficiency in Pre-Ranking: A Learnable Feature Selection based Approach

arXiv: https://arxiv.org/abs/2105.07706 · HTML: https://mlsanity.com/paper/2105.07706/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 8 of 8)

- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba](https://mlsanity.com/paper/1803.02349/) (2018)
- [A Deep Look into Neural Ranking Models for Information Retrieval](https://mlsanity.com/paper/1903.06902/) (2019)
- [COLD: Towards the Next Generation of Pre-Ranking System](https://mlsanity.com/paper/2007.16122/) (2020)
- [Cascade Ranking for Operational E-commerce Search](https://mlsanity.com/paper/1706.02093/) (2017)
- [Dropout Feature Ranking for Deep Learning Models](https://mlsanity.com/paper/1712.08645/) (2017)
- [PBODL : Parallel Bayesian Online Deep Learning for Click-Through Rate Prediction in Tencent Advertising System](https://mlsanity.com/paper/1707.00802/) (2017)
- [EENMF: An End-to-End Neural Matching Framework for E-Commerce Sponsored Search](https://mlsanity.com/paper/1812.01190/) (2018)

## Built on by (most cited first, 15 of 15)

- [COPR: Consistency-Oriented Pre-Ranking for Online Advertising](https://mlsanity.com/paper/2306.03516/) (2023)
- [Towards the Better Ranking Consistency: A Multi-task Learning Framework for Early Stage Ads Ranking](https://mlsanity.com/paper/2307.11096/) (2023)
- [Rethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models](https://mlsanity.com/paper/2310.08039/) (2023)
- [EasyRec: An easy-to-use, extendable and efficient framework for building industrial recommendation systems](https://mlsanity.com/paper/2209.12766/) (2022)
- [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)
- [Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems](https://mlsanity.com/paper/2206.12993/) (2022)
- [DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation](https://mlsanity.com/paper/2408.11611/) (2024)
- [Learning Partially-Decorrelated Common Spaces for Ad-hoc Video Search](https://mlsanity.com/paper/2508.02340/) (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)
- [HASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation](https://mlsanity.com/paper/2610.17368/) (2026)

## Abstract (arXiv)

In real-world search, recommendation, and advertising systems, the multi-stage ranking architecture is commonly adopted. Such architecture usually consists of matching, pre-ranking, ranking, and re-ranking stages. In the pre-ranking stage, vector-product based models with representation-focused architecture are commonly adopted to account for system efficiency. However, it brings a significant loss to the effectiveness of the system. In this paper, a novel pre-ranking approach is proposed which supports complicated models with interaction-focused architecture. It achieves a better tradeoff between effectiveness and efficiency by utilizing the proposed learnable Feature Selection method based on feature Complexity and variational Dropout (FSCD). Evaluations in a real-world e-commerce sponsored search system for a search engine demonstrate that utilizing the proposed pre-ranking, the effectiveness of the system is significantly improved. Moreover, compared to the systems with conventional pre-ranking models, an identical amount of computational resource is consumed.