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

• URL: http://arxiv.org/abs/2408.06883v3
• Date: Mon, 18 Aug 2025 10:08:02 GMT
• Title: Prompt-to-Slate: Diffusion Models for Prompt-Conditioned Slate Generation
• Authors: Federico Tomasi, Francesco Fabbri, Justin Carter, Elias Kalomiris, Mounia Lalmas, Zhenwen Dai,
• Abstract summary: We introduce DMSG, a generative framework based on diffusion models for prompt-conditioned slate generation.<n>Unlike retrieval-based or autoregressive models, DMSG models the joint distribution over slates, enabling greater flexibility and diversity.<n>We evaluate DMSG in two key domains: music playlist generation and e-commerce bundle creation.
• Score: 9.864273882854297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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