# Conditional Sequential Slate Optimization

arXiv: https://arxiv.org/abs/2108.05618 · HTML: https://mlsanity.com/paper/2108.05618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 12 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Top-K Off-Policy Correction for a REINFORCE Recommender System](https://mlsanity.com/paper/1812.02353/) (2018)
- [Unbiased Learning-to-Rank with Biased Feedback](https://mlsanity.com/paper/1608.04468/) (2016)
- [Introducing LETOR 4.0 Datasets](https://mlsanity.com/paper/1306.2597/) (2013)
- [Seq2Slate: Re-ranking and Slate Optimization with RNNs](https://mlsanity.com/paper/1810.02019/) (2018)
- [Reinforcement Learning to Rank in E-Commerce Search Engine: Formalization, Analysis, and Application](https://mlsanity.com/paper/1803.00710/) (2018)
- [Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation](https://mlsanity.com/paper/1911.03845/) (2019)
- [Large-scale Interactive Recommendation with Tree-structured Policy Gradient](https://mlsanity.com/paper/1811.05869/) (2018)
- [Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks](https://mlsanity.com/paper/1811.04415/) (2018)
- [Beyond Greedy Ranking: Slate Optimization via List-CVAE](https://mlsanity.com/paper/1803.01682/) (2018)
- [Addressing Purchase-Impression Gap through a Sequential Re-ranker](https://mlsanity.com/paper/2010.14570/) (2020)

## Built on by (most cited first, 1 of 1)

- [Adaptive Quality-Diversity Trade-offs for Large-Scale Batch Recommendation](https://mlsanity.com/paper/2602.02024/) (2026)

## Abstract (arXiv)

The top search results matching a user query that are displayed on the first page are critical to the effectiveness and perception of a search system. A search ranking system typically orders the results by independent query-document scores to produce a slate of search results. However, such unilateral scoring methods may fail to capture inter-document dependencies that users are sensitive to, thus producing a sub-optimal slate. Further, in practice, many real-world applications such as e-commerce search require enforcing certain distributional criteria at the slate-level, due to business objectives or long term user retention goals. Unilateral scoring of results does not explicitly support optimizing for such objectives with respect to a slate. Hence, solutions to the slate optimization problem must consider the optimal selection and order of the documents, along with adherence to slate-level distributional criteria. To that end, we propose a hybrid framework extended from traditional slate optimization to solve the conditional slate optimization problem. We introduce conditional sequential slate optimization (CSSO), which jointly learns to optimize for traditional ranking metrics as well as prescribed distribution criteria of documents within the slate. The proposed method can be applied to practical real world problems such as enforcing diversity in e-commerce search results, mitigating bias in top results and personalization of results. Experiments on public datasets and real-world data from e-commerce datasets show that CSSO outperforms popular comparable ranking methods in terms of adherence to distributional criteria while producing comparable or better relevance metrics.