# Revisit Recommender System in the Permutation Prospective

arXiv: https://arxiv.org/abs/2102.12057 · HTML: https://mlsanity.com/paper/2102.12057/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 16 of 16)

- [Cross DQN: Cross Deep Q Network for Ads Allocation in Feed](https://mlsanity.com/paper/2109.04353/) (2021)
- [Discrete Conditional Diffusion for Reranking in Recommendation](https://mlsanity.com/paper/2308.06982/) (2023)
- [PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://mlsanity.com/paper/2302.03487/) (2023)
- [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)
- [Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks](https://mlsanity.com/paper/2204.00888/) (2022)
- [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)
- [YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker](https://mlsanity.com/paper/2109.06436/) (2021)
- [Learning Distributions over Permutations and Rankings with Factorized Representations](https://mlsanity.com/paper/2505.24664/) (2025)
- [RewardRank: Optimizing True Learning-to-Rank Utility](https://mlsanity.com/paper/2508.14180/) (2025)
- [RIA: A Ranking-Infused Approach for Optimized listwise CTR Prediction](https://mlsanity.com/paper/2511.21394/) (2025)
- [FlashEvaluator: Expanding Search Space with Parallel Sequence-Level Evaluation](https://mlsanity.com/paper/2603.02565/) (2026)
- [Learning Permutation Distributions via Reflected Diffusion on Ranks](https://mlsanity.com/paper/2603.17353/) (2026)
- [Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking](https://mlsanity.com/paper/2604.07420/) (2026)
- [LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era](https://mlsanity.com/paper/2609.18148/) (2026)

## Abstract (arXiv)

Recommender systems (RS) work effective at alleviating information overload and matching user interests in various web-scale applications. Most RS retrieve the user's favorite candidates and then rank them by the rating scores in the greedy manner. In the permutation prospective, however, current RS come to reveal the following two limitations: 1) They neglect addressing the permutation-variant influence within the recommended results; 2) Permutation consideration extends the latent solution space exponentially, and current RS lack the ability to evaluate the permutations. Both drive RS away from the permutation-optimal recommended results and better user experience. To approximate the permutation-optimal recommended results effectively and efficiently, we propose a novel permutation-wise framework PRS in the re-ranking stage of RS, which consists of Permutation-Matching (PMatch) and Permutation-Ranking (PRank) stages successively. Specifically, the PMatch stage is designed to obtain the candidate list set, where we propose the FPSA algorithm to generate multiple candidate lists via the permutation-wise and goal-oriented beam search algorithm. Afterwards, for the candidate list set, the PRank stage provides a unified permutation-wise ranking criterion named LR metric, which is calculated by the rating scores of elaborately designed permutation-wise model DPWN. Finally, the list with the highest LR score is recommended to the user. Empirical results show that PRS consistently and significantly outperforms state-of-the-art methods. Moreover, PRS has achieved a performance improvement of 11.0% on PV metric and 8.7% on IPV metric after the successful deployment in one popular recommendation scenario of Taobao application.