# Language Models for Page-Level Layout Decisions in E-commerce Search

arXiv: https://arxiv.org/abs/2610.10920 · HTML: https://mlsanity.com/paper/2610.10920/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 5 of 5)

- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [ScreenAI: A Vision-Language Model for UI and Infographics Understanding](https://mlsanity.com/paper/2402.04615/) (2024)
- [MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces](https://mlsanity.com/paper/2510.08783/) (2025)
- [Large Language Models for Relevance Judgment in Product Search](https://mlsanity.com/paper/2406.00247/) (2024)
- [LLMJudge: LLMs for Relevance Judgments](https://mlsanity.com/paper/2408.08896/) (2024)

## Abstract (arXiv)

E-commerce search pages are critical touchpoints for millions of online shoppers. While traditional search engines return a ranked list of results, modern E-commerce search pages increasingly incorporate recommender system modules -- for example, secondary stacks that surface alternative product groupings at specific positions. When introduced appropriately, secondary stacks can improve user engagement; however, suboptimal placement may disrupt browsing flow and degrade the primary results. Unlike traditional search ranking, where evaluation techniques such as interleaving are well established, evaluating page-level layout changes e.g., when and where to insert a secondary stack remains challenging without costly online A/B testing. To address this, we study offline methods for evaluating whether a given layout decision -- specifically, the inclusion of a secondary stack at a particular position -- is beneficial to users. We investigate language models as scalable evaluators by comparing direct prompt-based, prompt-derived feature, and representation-based methods. Our results show that representation-based approaches consistently outperform prompt-based judging in predicting user engagement, suggesting they provide a reliable foundation for offline layout evaluation in E-commerce search.