# Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate Generation

arXiv: https://arxiv.org/abs/2408.06883 · HTML: https://mlsanity.com/paper/2408.06883/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion recommenders, one of 14 directions in Diffusion models.
- Builds on 20 of its 42 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Placed in

- Diffusion models / [Diffusion recommenders](https://mlsanity.com/diffusion/diffusion-recommenders/)

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Diffusion-LM Improves Controllable Text Generation](https://mlsanity.com/paper/2205.14217/) (2022)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [Self-conditioned Embedding Diffusion for Text Generation](https://mlsanity.com/paper/2211.04236/) (2022)
- [Diffusion Recommender Model](https://mlsanity.com/paper/2304.04971/) (2023)
- [Evaluating Stochastic Rankings with Expected Exposure](https://mlsanity.com/paper/2004.13157/) (2020)
- [Exploring the Role of Large Language Models in Prompt Encoding for Diffusion Models](https://mlsanity.com/paper/2406.11831/) (2024)
- [Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology](https://mlsanity.com/paper/1905.12767/) (2019)
- [Discrete Conditional Diffusion for Reranking in Recommendation](https://mlsanity.com/paper/2308.06982/) (2023)
- [Enhancing Recommendation Diversity by Re-ranking with Large Language Models](https://mlsanity.com/paper/2401.11506/) (2024)
- [Aligning Large Language Models with Recommendation Knowledge](https://mlsanity.com/paper/2404.00245/) (2024)
- [Relevance meets Diversity: A User-Centric Framework for Knowledge Exploration through Recommendations](https://mlsanity.com/paper/2408.03772/) (2024)
- [Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles](https://mlsanity.com/paper/2210.10085/) (2022)
- [Automatic Music Playlist Generation via Simulation-based Reinforcement Learning](https://mlsanity.com/paper/2310.09123/) (2023)

## Built on by (most cited first, 6 of 6)

- [A Survey on Diffusion Models for Recommender Systems](https://mlsanity.com/paper/2409.05033/) (2024)
- [Diffusion Models in Recommendation Systems: A Survey](https://mlsanity.com/paper/2501.10548/) (2025)
- [Efficient, Property-Aligned Fan-Out Retrieval via RL-Compiled Diffusion](https://mlsanity.com/paper/2603.06397/) (2026)
- [A Cascaded Generative Approach for e-Commerce Recommendations](https://mlsanity.com/paper/2605.11118/) (2026)
- [Stochastic Primal-Dual Decoding for Multiobjective Generative Recommender Systems](https://mlsanity.com/paper/2607.19357/) (2026)
- [LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era](https://mlsanity.com/paper/2609.18148/) (2026)

## Abstract (arXiv)

Slate generation is a common task in streaming and e-commerce platforms, where multiple items are presented together as a list or ``slate''. Traditional systems focus mostly on item-level ranking and often fail to capture the coherence of the slate as a whole. A key challenge lies in the combinatorial nature of selecting multiple items jointly. To manage this, conventional approaches often assume users interact with only one item at a time, assumption that breaks down when items are meant to be consumed together. In this paper, we introduce DMSG, a generative framework based on diffusion models for prompt-conditioned slate generation. DMSG learns high-dimensional structural patterns and generates coherent, diverse slates directly from natural language prompts. Unlike retrieval-based or autoregressive models, DMSG models the joint distribution over slates, enabling greater flexibility and diversity. We evaluate DMSG in two key domains: music playlist generation and e-commerce bundle creation. In both cases, DMSG produces high-quality slates from textual prompts without explicit personalization signals. Offline and online results show that DMSG outperforms strong baselines in both relevance and diversity, offering a scalable, low-latency solution for prompt-driven recommendation. A live A/B test on a production playlist system further demonstrates increased user engagement and content diversity.