# Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search

arXiv: https://arxiv.org/abs/2602.02514 · HTML: https://mlsanity.com/paper/2602.02514/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 7 of 7)

- [Double/Debiased Machine Learning for Treatment and Causal Parameters](https://mlsanity.com/paper/1608.00060/) (2016)
- [An Efficient Bandit Algorithm for Realtime Multivariate Optimization](https://mlsanity.com/paper/1810.09558/) (2018)
- [Learning to Rank in the Position Based Model with Bandit Feedback](https://mlsanity.com/paper/2004.13106/) (2020)
- [A Map of Bandits for E-commerce](https://mlsanity.com/paper/2107.00680/) (2021)
- [A Batched Multi-Armed Bandit Approach to News Headline Testing](https://mlsanity.com/paper/1908.06256/) (2019)
- [Incorporating Clicks, Attention and Satisfaction into a Search Engine Result Page Evaluation Model](https://mlsanity.com/paper/1609.00552/) (2016)
- [Cooperative Multi-Agent Deep Reinforcement Learning in Content Ranking Optimization](https://mlsanity.com/paper/2408.04251/) (2024)

## Abstract (arXiv)

E-commerce Search Results Pages (SRPs) are evolving from linear lists to complex, non-linear layouts, rendering traditional position-biased ranking models insufficient. Moreover, existing optimization frameworks typically maximize short-term signals (e.g., clicks, same-day revenue) because long-term satisfaction metrics (e.g., expected two-week revenue) involve delayed feedback and challenging long-horizon credit attribution. To bridge these gaps, we propose a novel Whole-Page Experience Optimization Framework. Unlike traditional list-wise rankers, our approach explicitly models the interplay between item relevance, 2D positional layout, and visual elements. We use a causal framework to develop metrics for measuring long-term user satisfaction based on quasi-experimental data. We validate our approach through industry-scale A/B testing, where the model demonstrated a 1.86% improvement in brand relevance (our primary customer experience metric) while simultaneously achieving a statistically significant revenue uplift of +0.05%