优化电商搜索页整体体验，兼顾长期用户满意度与收入增长。
Design and Evaluation of Whole-Page Experience Optimization for E-commerce Search
- 构建全页面体验优化框架，融合相关性、布局位置与视觉元素
- 通过准实验数据建模，实现对两周内收入等长期指标的量化评估
- 实测提升品牌相关性1.86%，同时带来0.05%显著收入增长
电商搜索结果页正从线性列表演变为复杂非线性布局，传统基于位置偏倚的排序模型已不适用。现有优化框架多聚焦短期指标（如点击率、当日营收），因长期满意度指标（如预期两周内营收）存在反馈延迟和长期信用归因难题而难以有效优化。为此，我们提出一种全新的全页面体验优化框架。该框架不同于传统列表级排序器，显式建模商品相关性、二维布局位置与视觉元素之间的相互作用。采用因果推断框架，基于准实验数据构建长期用户满意度度量方法。在工业规模的A/B测试中验证，该模型在主用户体验指标——品牌相关性上提升1.86%，同时实现统计显著的0.05%营收增长。
原文摘要 · Abstract (English)
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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