# Seq2Slate: Re-ranking and Slate Optimization with RNNs

arXiv: https://arxiv.org/abs/1810.02019 · HTML: https://mlsanity.com/paper/1810.02019/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2018.

## Built on by (most cited first, 30 of 39)

- [Discrete Conditional Diffusion for Reranking in Recommendation](https://mlsanity.com/paper/2308.06982/) (2023)
- [Non-autoregressive Generative Models for Reranking Recommendation](https://mlsanity.com/paper/2402.06871/) (2024)
- [PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation](https://mlsanity.com/paper/2203.12267/) (2022)
- [Make Large Language Model a Better Ranker](https://mlsanity.com/paper/2403.19181/) (2024)
- [Variation Control and Evaluation for Generative SlateRecommendations](https://mlsanity.com/paper/2102.13302/) (2021)
- [UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning](https://mlsanity.com/paper/2108.00354/) (2021)
- [PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://mlsanity.com/paper/2302.03487/) (2023)
- [Set-to-Sequence Methods in Machine Learning: a Review](https://mlsanity.com/paper/2103.09656/) (2021)
- [Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Sampling](https://mlsanity.com/paper/2104.15046/) (2021)
- [Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention](https://mlsanity.com/paper/2105.10019/) (2021)
- [Combining Reward and Rank Signals for Slate Recommendation](https://mlsanity.com/paper/2107.12455/) (2021)
- [A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2309.01194/) (2023)
- [NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems](https://mlsanity.com/paper/2502.06097/) (2025)
- [Context Uncertainty in Contextual Bandits with Applications to Recommender Systems](https://mlsanity.com/paper/2202.00805/) (2022)
- [Relative Advantage Debiasing for Watch-Time Prediction in Short-Video Recommendation](https://mlsanity.com/paper/2508.11086/) (2025)
- [GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction](https://mlsanity.com/paper/2510.25220/) (2025)
- [An Attention Module for Convolutional Neural Networks](https://mlsanity.com/paper/2108.08205/) (2021)
- [Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation](https://mlsanity.com/paper/2603.07107/) (2026)
- [Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System](https://mlsanity.com/paper/2112.07621/) (2021)
- [Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendation](https://mlsanity.com/paper/2303.01671/) (2023)
- [A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Search](https://mlsanity.com/paper/2405.15521/) (2024)
- [Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search](https://mlsanity.com/paper/2408.05751/) (2024)
- [Conditional Sequential Slate Optimization](https://mlsanity.com/paper/2108.05618/) (2021)
- [Page-level Optimization of e-Commerce Item Recommendations](https://mlsanity.com/paper/2108.05891/) (2021)
- [Coordinating CAV Swarms at Intersections with a Deep Learning Model](https://mlsanity.com/paper/2211.05297/) (2022)
- [Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Ranking](https://mlsanity.com/paper/2305.00162/) (2023)
- [MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems](https://mlsanity.com/paper/2401.06293/) (2024)
- [Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models](https://mlsanity.com/paper/2601.13533/) (2026)
- [Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model](https://mlsanity.com/paper/2112.00333/) (2021)
- [Edge-Compatible Reinforcement Learning for Recommendations](https://mlsanity.com/paper/2112.05812/) (2021)

## Abstract (arXiv)

Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be placed alongside it. In this work, we propose a sequence-to-sequence model for ranking called seq2slate. At each step, the model predicts the next `best' item to place on the slate given the items already selected. The sequential nature of the model allows complex dependencies between the items to be captured directly in a flexible and scalable way. We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data. We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.