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

• URL: http://arxiv.org/abs/2602.02514v1
• Date: Fri, 23 Jan 2026 07:41:04 GMT
• Title: Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search
• Authors: Pratik Lahiri, Bingqing Ge, Zhou Qin, Aditya Jumde, Shuning Huo, Lucas Scottini, Yi Liu, Mahmoud Mamlouk, Wenyang Liu,
• Abstract summary: E-commerce search results pages (SRPs) are evolving from linear lists to complex, non-linear layouts.<n>We propose a novel Whole-Page Experience Optimization Framework to bridge the gap between short-term signals and long-term satisfaction metrics.<n>We validate our approach through industry-scale A/B testing, where the model demonstrated a 1.86% improvement in brand relevance.
• Score: 4.089644567431606
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%

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