# PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce

arXiv: https://arxiv.org/abs/2302.03487 · HTML: https://mlsanity.com/paper/2302.03487/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 6 Feb 2023.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [Deep Interest Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1706.06978/) (2017)
- [Deep & Cross Network for Ad Click Predictions](https://mlsanity.com/paper/1708.05123/) (2017)
- [Deep Interest Evolution Network for Click-Through Rate Prediction](https://mlsanity.com/paper/1809.03672/) (2018)
- [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://mlsanity.com/paper/1803.05170/) (2018)
- [Learning Tree-based Deep Model for Recommender Systems](https://mlsanity.com/paper/1801.02294/) (2018)
- [Learning a Deep Listwise Context Model for Ranking Refinement](https://mlsanity.com/paper/1804.05936/) (2018)
- [End-to-End User Behavior Retrieval in Click-Through RatePrediction Model](https://mlsanity.com/paper/2108.04468/) (2021)
- [Seq2Slate: Re-ranking and Slate Optimization with RNNs](https://mlsanity.com/paper/1810.02019/) (2018)
- [COLD: Towards the Next Generation of Pre-Ranking System](https://mlsanity.com/paper/2007.16122/) (2020)
- [Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction](https://mlsanity.com/paper/2205.10249/) (2022)
- [Real-time Short Video Recommendation on Mobile Devices](https://mlsanity.com/paper/2208.09577/) (2022)
- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search](https://mlsanity.com/paper/1805.08524/) (2018)
- [Revisit Recommender System in the Permutation Prospective](https://mlsanity.com/paper/2102.12057/) (2021)
- [GRN: Generative Rerank Network for Context-wise Recommendation](https://mlsanity.com/paper/2104.00860/) (2021)
- [Hierarchically Constrained Adaptive Ad Exposure in Feeds](https://mlsanity.com/paper/2205.15759/) (2022)
- [Hit ratio: An Evaluation Metric for Hashtag Recommendation](https://mlsanity.com/paper/2010.01258/) (2020)
- [NMA: Neural Multi-slot Auctions with Externalities for Online Advertising](https://mlsanity.com/paper/2205.10018/) (2022)
- [Decision-Making Context Interaction Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2301.12402/) (2023)

## Built on by (most cited first, 8 of 8)

- [Non-autoregressive Generative Models for Reranking Recommendation](https://mlsanity.com/paper/2402.06871/) (2024)
- [NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems](https://mlsanity.com/paper/2502.06097/) (2025)
- [GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction](https://mlsanity.com/paper/2510.25220/) (2025)
- [Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation](https://mlsanity.com/paper/2603.07107/) (2026)
- [Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search](https://mlsanity.com/paper/2408.05751/) (2024)
- [RewardRank: Optimizing True Learning-to-Rank Utility](https://mlsanity.com/paper/2508.14180/) (2025)
- [DeGRe: Dense-supervised Generative Reranking for Recommendation](https://mlsanity.com/paper/2605.25749/) (2026)
- [LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era](https://mlsanity.com/paper/2609.18148/) (2026)

## Abstract (arXiv)

Re-ranking draws increased attention on both academics and industries, which rearranges the ranking list by modeling the mutual influence among items to better meet users' demands. Many existing re-ranking methods directly take the initial ranking list as input, and generate the optimal permutation through a well-designed context-wise model, which brings the evaluation-before-reranking problem. Meanwhile, evaluating all candidate permutations brings unacceptable computational costs in practice. Thus, to better balance efficiency and effectiveness, online systems usually use a two-stage architecture which uses some heuristic methods such as beam-search to generate a suitable amount of candidate permutations firstly, which are then fed into the evaluation model to get the optimal permutation. However, existing methods in both stages can be improved through the following aspects. As for generation stage, heuristic methods only use point-wise prediction scores and lack an effective judgment. As for evaluation stage, most existing context-wise evaluation models only consider the item context and lack more fine-grained feature context modeling. This paper presents a novel end-to-end re-ranking framework named PIER to tackle the above challenges which still follows the two-stage architecture and contains two mainly modules named FPSM and OCPM. We apply SimHash in FPSM to select top-K candidates from the full permutation based on user's permutation-level interest in an efficient way. Then we design a novel omnidirectional attention mechanism in OCPM to capture the context information in the permutation. Finally, we jointly train these two modules end-to-end by introducing a comparative learning loss. Offline experiment results demonstrate that PIER outperforms baseline models on both public and industrial datasets, and we have successfully deployed PIER on Meituan food delivery platform.