# Beyond Greedy Ranking: Slate Optimization via List-CVAE

arXiv: https://arxiv.org/abs/1803.01682 · HTML: https://mlsanity.com/paper/1803.01682/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2018.

## Built on by (most cited first, 21 of 21)

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- [Generative Slate Recommendation with Reinforcement Learning](https://mlsanity.com/paper/2301.08632/) (2023)
- [Variation Control and Evaluation for Generative SlateRecommendations](https://mlsanity.com/paper/2102.13302/) (2021)
- [Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation](https://mlsanity.com/paper/2301.11489/) (2023)
- [Probabilistic Rank and Reward: A Scalable Model for Slate Recommendation](https://mlsanity.com/paper/2208.06263/) (2022)
- [Exposing Product Bias in LLM Investment Recommendation](https://mlsanity.com/paper/2503.08750/) (2025)
- [Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching](https://mlsanity.com/paper/2101.06423/) (2021)
- [Combining Reward and Rank Signals for Slate Recommendation](https://mlsanity.com/paper/2107.12455/) (2021)
- [NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems](https://mlsanity.com/paper/2502.06097/) (2025)
- [Explore User Neighborhood for Real-time E-commerce Recommendation](https://mlsanity.com/paper/2103.00442/) (2021)
- [Off-Policy Evaluation of Slate Policies under Bayes Risk](https://mlsanity.com/paper/2101.02553/) (2021)
- [Conditional Sequential Slate Optimization](https://mlsanity.com/paper/2108.05618/) (2021)
- [MultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems](https://mlsanity.com/paper/2401.06293/) (2024)
- [HiGR: Industrial-Scale Hierarchical Generative Slate Recommendation Framework in Tencent](https://mlsanity.com/paper/2512.24787/) (2025)
- [Edge-Compatible Reinforcement Learning for Recommendations](https://mlsanity.com/paper/2112.05812/) (2021)
- [Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions](https://mlsanity.com/paper/2406.10795/) (2024)
- [Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking](https://mlsanity.com/paper/2604.07420/) (2026)
- [Sequential Multimodal Evidence Optimization for Product Media Ranking in E-Commerce](https://mlsanity.com/paper/2608.15662/) (2026)
- [LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era](https://mlsanity.com/paper/2609.18148/) (2026)

## Abstract (arXiv)

The conventional solution to the recommendation problem greedily ranks individual document candidates by prediction scores. However, this method fails to optimize the slate as a whole, and hence, often struggles to capture biases caused by the page layout and document interdepedencies. The slate recommendation problem aims to directly find the optimally ordered subset of documents (i.e. slates) that best serve users' interests. Solving this problem is hard due to the combinatorial explosion in all combinations of document candidates and their display positions on the page. Therefore we propose a paradigm shift from the traditional viewpoint of solving a ranking problem to a direct slate generation framework. In this paper, we introduce List Conditional Variational Auto-Encoders (List-CVAE), which learns the joint distribution of documents on the slate conditioned on user responses, and directly generates full slates. Experiments on simulated and real-world data show that List-CVAE outperforms popular comparable ranking methods consistently on various scales of documents corpora.