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%
@article{arxiv.2602.02514,
title = {Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search},
author = {Pratik Lahiri and Bingqing Ge and Zhou Qin and Aditya Jumde and Shuning Huo and Lucas Scottini and Yi Liu and Mahmoud Mamlouk and Wenyang Liu},
journal= {arXiv preprint arXiv:2602.02514},
year = {2026}
}