构建内容的未来：深入了解Booking.com的智能内容丰富平台

Oh, the sweet nineties. Back then, the internet had only simple tags for pictures, and booking your summer vacation meant calling a random hotel and hoping for the best. It’s now 2024, and both things have become much smarter. How smart, you ask? Read on to discover how Booking.com platforms advanced machine learning algorithms to enhance content selection, delivering a more personalized customer experience.

哦，甜美的九十年代。那时，互联网只有简单的标签用于图片，预订你的暑假意味着打电话给一家随机的酒店并希望得到最好的结果。现在是2024年，这两件事都变得更加智能。你问有多智能？继续阅读，了解Booking.com平台如何利用先进的机器学习算法来增强内容选择，提供更个性化的客户体验。

In this article, we’ll examine the architecture of Booking.com’s Content Intelligence Platform, a system designed to maximize the use of photos and text.

在本文中，我们将探讨Booking.com的内容智能平台的架构，这是一个旨在最大化使用照片和文字的系统。

The Evolution of Content-Related ML in Booking.com

Booking.com内容相关机器学习的演变

Users of Booking.com engage with content from the moment they land on the site until the moment of truth: ordering their next vacation. The homepage greets you with pictures of trending destinations. When you seek unbiased info about the property, reviews of other guests await. And, of course, no one books a stay without checking the room’s pictures first. The property owner can either select all this content or let AI select it (can you guess which option brings better results? ;) ).

Booking.com的用户从他们登陆网站的那一刻起到预订下一次假期的关键时刻，都会与内容互动。主页会用热门目的地的图片迎接你。当你寻找关于物业的公正信息时，其他客人的评论在等着你。当然，没有人会在不先查看房间照片的情况下预订住宿。物业所有者可以选择所有这些内容，或者让AI选择它（你能猜到哪种选择带来更好的结果吗？;)）。

As Machine Learning models gained popularity, more teams added ML features to their products. One team used different quality models to rearrange images to increase diversity and better represent users. Another team used a mix of models to suggest improved photos selection to hotel owners. However, many teams needed more resources to develop similar models. They couldn’t use the existing ones, which left unmet needs. This process highlighted the need for accessible and generic Machine Learning models that could serve multiple teams.

随着机器学习模型的普及，越来越多的团队将机器学...