解锁定制化的力量：我们的丰富系统如何转变推荐数据丰富 | 作者：Juan Pablo Lorenzo | Booking.com 工程 | 2025年6月
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How are accurate property prices on Booking.com connected to machine learning that recommends appealing property photos? What about the number of users who have wishlisted a property? And how can developers assess if their recommendation models effectively boost traveler clicks? None of these pieces of information are recommendations on their own, but they’re crucial when providing our travelers good recommendations. For years, our Recommendation Platform has handled this process, but we needed a better way.
Booking.com上的准确房产价格与推荐吸引人房产照片的机器学习有何关联？愿望清单中有多少用户关注某个房产呢？开发人员如何评估他们的推荐模型是否有效提升了旅行者的点击率？这些信息本身并不是推荐，但在为我们的旅行者提供良好推荐时，它们是至关重要的。多年来，我们的推荐平台一直在处理这个过程，但我们需要更好的方法。
Recommendation Platform is a service created to empower teams with adaptive, scalable, and personalized recommendations. These recommendations are integrated into every step of the customer journey, covering everything from attractions and flights to travel destinations and accommodation. We allow teams to use machine learning models combining multiple providers to achieve traveler recommendations. The platform is developed to be self-served and new use cases could be implemented by those teams. Check out our Self-Serve Platform for Scalable ML Recommendations article for a deeper look into how the platform works.
推荐平台是一个旨在赋能团队提供自适应、可扩展和个性化推荐的服务。这些推荐融入了客户旅程的每一个步骤，涵盖从景点和航班到旅行目的地和住宿的所有内容。我们允许团队使用结合多个提供商的机器学习模型来实现旅行者推荐。该平台旨在自助服务，新的用例可以由这些团队实施。请查看我们的可扩展ML推荐的自助平台文章，以深入了解该平台的工作原理。
Recommendations are valuable by themselves, but in most cases, information about the specific recommendation is just as important. Previously, adding such information was complex, lacked isolation, and had limited reusability. The new Enrichment System aims to overcome these challenges.
推荐本身是有价值的，但在大多数情况下，关于特定推荐的信息同样重要。之前，添加此类信息的过程复杂，缺乏隔离，并且可重用性有限。新的 增强...