Online Travel Platforms are virtual two-sided marketplaces where guests search for accommodations and accommodation providers list their properties such as hotels and vacation rentals. The large majority of hotels are rated by official institutions with a number of stars indicating the quality of service they provide. It is a simple and effective mechanism that contributes to match supply with demand by helping guests to find options meeting their criteria and accommodation suppliers to market their product to the right segment directly impacting the number of transactions on the platform. Unfortunately, no similar rating system exists for the large majority of vacation rentals, making it difficult for guests to search and compare options and hard for vacation rentals suppliers to market their product effectively. In this work we describe a machine learned quality rating system for vacation rentals. The problem is challenging, mainly due to explainability requirements and the lack of ground truth. We present techniques to address these challenges and empirical evidence of their efficacy. Our system was successfully deployed and validated through Online Controlled Experiments performed in Booking. com, a large Online Travel Platform, and running for more than one year, impacting more than a million accommodations and millions of guests.
翻译:在线旅行平台是虚拟的双面市场,客客人们在网上寻找住宿和住宿供应商寻找其房产,如旅馆和休假租赁等。大多数酒店被官方机构评级,官方机构有数个明星,显示其服务质量。这是一个简单而有效的机制,通过帮助客人们找到符合其标准的备选方案和住宿供应商将产品推销到右部分直接影响到平台交易数量,从而有助于满足需求。不幸的是,绝大多数休假租赁都不存在类似的评级制度,使客人们难以搜索和比较各种选择,也难以让度假租赁供应商有效销售产品。在这项工作中,我们描述了一个机器学习的度假租赁质量评级制度。主要由于可解释性要求和缺乏地面真相,问题十分棘手。我们提出了应对这些挑战的技术及其功效的经验证据。我们的系统通过在预订.comm(一个大型在线旅行平台)进行的在线控制实验成功部署和验证,并运行了一年以上,影响到100多万个住宿和数百万名游客。