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.

In online marketplaces, search ranking's objective is not only to purchase or conversion (primary objective), but to also the purchase outcomes(secondary objectives), e.g. order cancellation(or return), review rating, customer service inquiries, platform long term growth. Multi-objective learning to rank has been widely studied to balance primary and secondary objectives. But traditional approaches in industry face some challenges including expensive parameter tuning leads to sub-optimal solution, suffering from imbalanced data sparsity issue, and being not compatible with ad-hoc objective. In this paper, we propose a distillation-based ranking solution for multi-objective ranking, which optimizes the end-to-end ranking system at Airbnb across multiple ranking models on different objectives along with various considerations to optimize training and serving efficiency to meet industry standards. We found it performs much better than traditional approaches, it doesn't only significantly increases primary objective by a large margin but also meet secondary objectives constraints and improve model stability. We also demonstrated the proposed system could be further simplified by model self-distillation. Besides this, we did additional simulations to show that this approach could also help us efficiently inject ad-hoc non-differentiable business objective into the ranking system while enabling us to balance our optimization objectives.

At Airbnb, an online marketplace for stays and experiences, guests often spend weeks exploring and comparing multiple items before making a final reservation request. Each reservation request may then potentially be rejected or cancelled by the host prior to check-in. The long and exploratory nature of the search journey, as well as the need to balance both guest and host preferences, present unique challenges for Airbnb search ranking. In this paper, we present Journey Ranker, a new multi-task deep learning model architecture that addresses these challenges. Journey Ranker leverages intermediate guest actions as milestones, both positive and negative, to better progress the guest towards a successful booking. It also uses contextual information such as guest state and search query to balance guest and host preferences. Its modular and extensible design, consisting of four modules with clear separation of concerns, allows for easy application to use cases beyond the Airbnb search ranking context. We conducted offline and online testing of the Journey Ranker and successfully deployed it in production to four different Airbnb products with significant business metrics improvements.