让搜索排名更懂用户比价行为，提升预订转化率。
Learning to Comparison-Shop
- 设计新模型显式学习用户比价习惯，而非孤立评估商品。
- 线上测试显示NDCG提升1.7%，预订转化率提高0.6%。
- 适合关注搜索排序与用户行为对齐的电商平台研究者。
在Airbnb等在线市场中，用户常通过比价做决策，但主流电商搜索引擎仍以孤立方式评估商品，忽略用户在结果页对比的上下文。尽管深度学习提升了排序精度、多样性与公平性，但仍未有效解决与用户比价行为对齐的问题。本文提出一种新排名架构——学习比价购物（LTCS）系统，显式建模并学习用户比价行为。大量离线与线上实验表明，该方法在关键业务指标上取得显著提升：NDCG提升1.7%，A/B测试中预订转化率提高0.6%，同时改善用户体验。与现有先进方法相比，LTCS表现显著更优。
原文摘要 · Abstract (English)
In online marketplaces like Airbnb, users frequently engage in comparison shopping before making purchase decisions. Despite the prevalence of this behavior, a significant disconnect persists between mainstream e-commerce search engines and users' comparison needs. Traditional ranking models often evaluate items in isolation, disregarding the context in which users compare multiple items on a search results page. While recent advances in deep learning have sought to improve ranking accuracy, diversity, and fairness by encoding listwise context, the challenge of aligning search rankings with user comparison shopping behavior remains inadequately addressed. In this paper, we propose a novel ranking architecture - Learning-to-Comparison-Shop (LTCS) System - that explicitly models and learns users' comparison shopping behaviors. Through extensive offline and online experiments, we demonstrate that our approach yields statistically significant gains in key business metrics - improving NDCG by 1.7% and boosting booking conversion rate by 0.6% in A/B testing - while also enhancing user experience. We also compare our model against state-of-the-art approaches and demonstrate that LTCS significantly outperforms them.
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