可自助服务的可扩展机器学习推荐平台

The Recommendation Platform (RecP) has been shaping user experiences for over two years, expertly translating customer needs into personalized recommendations. This evolving platform is integral to key areas of our Booking.com website and apps, such as the Homepage and Search Results pages, and is backed by a dedicated team and a robust pipeline solution.

Recommendation Platform (RecP) 已经塑造用户体验超过两年，专业地将客户需求转化为个性化推荐。这个不断发展的平台是我们Booking.com网站和应用程序关键领域的组成部分，如主页和搜索结果页面，并由一个专门的团队和一个强大的管道解决方案支持。

An example of a recommendations carousel for destinations

目的地推荐轮播示例

The Driving Force: Challenges and Solutions

驱动力：挑战和解决方案

We will focus on specific examples, though we actually have a wide range of different problems and use cases. Consider the task of recommending local travel destinations for a customer. For new customers, it’s most likely essential to use their origin country as the starting point for travel recommendations. However, for returning customers with prior website activity, such as search history, recommendations should be based on their past interests and recent searches. While gathering the right data is crucial, the real challenge lies in optimizing the predictions we present to each customer.

我们将专注于具体的例子，尽管我们实际上有各种不同的问题和用例。考虑为客户推荐本地旅游目的地的任务。对于新客户，最有可能的是使用他们的原籍国作为旅游推荐的起点。然而，对于有过网站活动的回头客，例如搜索历史，推荐应该基于他们过去的兴趣和最近的搜索。虽然收集正确的数据至关重要，但真正的挑战在于优化我们向每个客户展示的预测。

The following image shows an example of two options related to the customer’s activity based on their location.

下图显示了根据客户位置提供的两个活动选项示例。

In other words, the customer’s level of activity on the website, along with their location, plays an important role in decision-making, but it is not the only factor. Since the machine-learning models are trained on specific data from a certain period, the quality of the predictions is high, but not perfect, as additional dynamic factors can influence decisions. These factors, such as availability or other variables, can change at any moment. Considering these parameters leads to more efficient, accurate, and personalized decisi...