Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915769889062912 |
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| author | Lahiri, Pratik Ge, Bingqing Qin, Zhou Jumde, Aditya Huo, Shuning Scottini, Lucas Liu, Yi Mamlouk, Mahmoud Liu, Wenyang |
| author_facet | Lahiri, Pratik Ge, Bingqing Qin, Zhou Jumde, Aditya Huo, Shuning Scottini, Lucas Liu, Yi Mamlouk, Mahmoud Liu, Wenyang |
| contents | 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% |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02514 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search Lahiri, Pratik Ge, Bingqing Qin, Zhou Jumde, Aditya Huo, Shuning Scottini, Lucas Liu, Yi Mamlouk, Mahmoud Liu, Wenyang Information Retrieval Machine Learning 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% |
| title | Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2602.02514 |