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.

翻译：在诸如Airbnb等在线市场中，用户在做出购买决策前频繁进行比价购物。尽管这种行为普遍存在，主流电子商务搜索引擎与用户的比价需求之间仍存在显著脱节。传统排序模型通常孤立地评估商品，忽视了用户在搜索结果页面上比较多个商品的上下文情境。虽然深度学习的最新进展通过编码列表上下文来提升排序准确性、多样性和公平性，但使搜索排序与用户比价购物行为保持一致这一挑战仍未得到充分解决。本文提出了一种新颖的排序架构——学习比较购物（LTCS）系统，该系统明确建模并学习用户的比价购物行为。通过大量离线和在线实验，我们证明该方法在关键业务指标上取得了统计学显著的提升——在A/B测试中将NDCG提高了1.7%，并将预订转化率提升了0.6%——同时改善了用户体验。我们还与最先进的方法进行了比较，结果表明LTCS显著优于这些方法。