mlsanityData to 13 Oct 2026

Seq2Slate: Re-ranking and Slate Optimization with RNNs

arXiv 1810.02019 ↗First posted 4 Oct 2018cs.IR cs.LG stat.MLcited by 39

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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.

Key facts. Cited by 39 papers in the map (data through 13 Oct 2026). First posted on arXiv 4 Oct 2018.

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Citing papers per year: 2021: 12; 2022: 5; 2023: 6; 2024: 7; 2025: 3; 2026: 6.

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  1. 2023Discrete Conditional Diffusion for Reranking in Recommendationcited by 19
  2. 2024Non-autoregressive Generative Models for Reranking Recommendationcited by 17
  3. 2022PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendationcited by 12
  4. 2024Make Large Language Model a Better Rankercited by 11
  5. 2021Variation Control and Evaluation for Generative SlateRecommendationscited by 10
  6. 2021UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learningcited by 10
  7. 2023PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commercecited by 8
  8. 2021Set-to-Sequence Methods in Machine Learning: a Reviewcited by 6
  9. 2021Dynamic Slate Recommendation with Gated Recurrent Units and Thompson Samplingcited by 5
  10. 2021Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attentioncited by 5
  11. 2021Combining Reward and Rank Signals for Slate Recommendationcited by 5
  12. 2023A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospectscited by 5
27 more
  1. 2025NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systemscited by 5
  2. 2022Context Uncertainty in Contextual Bandits with Applications to Recommender Systemscited by 4
  3. 2025Relative Advantage Debiasing for Watch-Time Prediction in Short-Video Recommendationcited by 4
  4. 2025GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Predictioncited by 4
  5. 2021An Attention Module for Convolutional Neural Networkscited by 3
  6. 2026Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillationcited by 3
  7. 2021Re-ranking With Constraints on Diversified Exposures for Homepage Recommender Systemcited by 2
  8. 2023Tile Networks: Learning Optimal Geometric Layout for Whole-page Recommendationcited by 2
  9. 2024A Preference-oriented Diversity Model Based on Mutual-information in Re-ranking for E-commerce Searchcited by 2
  10. 2024Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Searchcited by 2
  11. 2021Conditional Sequential Slate Optimizationcited by 1
  12. 2021Page-level Optimization of e-Commerce Item Recommendationscited by 1
  13. 2022Coordinating CAV Swarms at Intersections with a Deep Learning Modelcited by 1
  14. 2023Beyond Prediction: On-street Parking Recommendation using Heterogeneous Graph-based List-wise Rankingcited by 1
  15. 2024MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systemscited by 1
  16. 2021Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Modelcited by 0
  17. 2021Edge-Compatible Reinforcement Learning for Recommendationscited by 0
  18. 2022Personalized Execution Time Optimization for Billion-Scale Scheduled Jobscited by 0
  19. 2022Intra-session Context-aware Feed Recommendation in Live Systemscited by 0
  20. 2023Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rankcited by 0
  21. 2024Adaptively Learning to Select-Rank in Online Platformscited by 0
  22. 2024Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commercecited by 0
  23. 2026Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Modelscited by 0
  24. 2026Dual-Rerank: Fusing Causality and Utility for Industrial Generative Rerankingcited by 0
  25. 2026Sequential Multimodal Evidence Optimization for Product Media Ranking in E-Commercecited by 0
  26. 2026PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Taskscited by 0
  27. 2026LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Eracited by 0