cs.LG提交于 2026-10-09已译

用于电子商务搜索页面级布局决策的语言模型

Language Models for Page-Level Layout Decisions in E-commerce Search

Varun Joshi · Eva C. Song · ChengXiang Zhai

中文摘要

电商搜索页面是数百万在线购物者的关键触点。传统搜索引擎返回排序结果列表,而现代电商搜索页面越来越多地引入推荐系统模块——例如在特定位置呈现替代商品分组的二级堆栈(secondary stack)。二级堆栈在合适位置引入可提升用户参与度,但放置不当会破坏浏览流畅性并降低主结果质量。与传统搜索排序不同,页面级布局变更的评估缺乏成熟的离线和低成本手段,通常依赖昂贵的在线 A/B 测试。为解决这一问题,本研究针对给定布局决策——具体为在某位置插入二级堆栈——是否对用户有利,系统研究了离线评估方法。通过对比直接提示式、提示派生特征式与表示式(representation-based)三种范式,考察语言模型作为可扩展评估器的可行性。结果表明,表示式方法在预测用户参与度上持续优于基于提示的判别式,显示其能为电商搜索中的离线布局评估提供可靠基础。

关键要点

01问题:电商搜索页面插入二级堆栈的布局决策缺低成本离线评估手段,传统排序的 interleaving 等方法不适用
02方法:对比直接 prompt、prompt 派生特征、表示式三种语言模型评估范式,用于预测布局变更的用户参与度
03结果:表示式方法在用户参与度预测上持续优于基于提示的判别式
04含义:表示式语言模型可作为电商搜索离线页面级布局评估的可靠基础,有望替代部分 A/B 测试
05局限:研究聚焦离线评估,实际部署仍需在线 A/B 试验验证,且评估对象限于二级堆栈插入位置

解读

尚无解读。

原始英文摘要

arXiv:2610.10920v1 Announce Type: new Abstract: E-commerce search pages are critical touchpoints for millions of online shoppers. While traditional search engines return a ranked list of results, modern E-commerce search pages increasingly incorporate recommender system modules -- for example, secondary stacks that surface alternative product groupings at specific positions. When introduced appropriately, secondary stacks can improve user engagement; however, suboptimal placement may disrupt browsing flow and degrade the primary results. Unlike traditional search ranking, where evaluation techniques such as interleaving are well established, evaluating page-level layout changes e.g., when and where to insert a secondary stack remains challenging without costly online A/B testing. To address this, we study offline methods for evaluating whether a given layout decision -- specifically, the inclusion of a secondary stack at a particular position -- is beneficial to users. We investigate language models as scalable evaluators by comparing direct prompt-based, prompt-derived feature, and representation-based methods. Our results show that representation-based approaches consistently outperform prompt-based judging in predicting user engagement, suggesting they provide a reliable foundation for offline layout evaluation in E-commerce search.