推荐系统如何处理非常大的数据？
如何在大型系统上比较功能？例如，当我在谷歌上搜索时，谷歌是否会将我的请求与所有网站进行比较？或者只是一些特定的平台，例如 Netflix 或 Youtube。它会一一扫描所有视频以检测这些视频对我来说有多好吗？
How are features compared on large systems? For example, when I search on google, does google compare my request against all the web sites? Or just some specific platforms like Netflix or Youtube. Does it scan all the videos one by one to detect how good the videos are for me?
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它不是那样工作的。它是通过机器学习完成的。
它的作用是获取大量信息并从其他人那里获取相似数据。然后将其应用到您的选择上。
It doesn't work like that. It is done by Machine Learning.
What it does is it takes a lot of information and get similarities data from other people.And then it applies it on your choice.
这是一个很好的问题！
并非您列出的所有服务都以相同的方式工作。谷歌做了一种叫做索引的东西，它基本上是以一种可以更有效地查找网站的方式存储网站。 Discord 也对消息执行此操作，如果您使用它，您可能已经注意到了。
Netflix 有大约 2000 个节目，而网站有数百万个，可能还有数十亿个 Google 索引页面。因此，进行 Netflix 搜索要简单得多，并且可能不需要太多索引或花哨的操作。
如果您对 Netflix 和 YouTube 等网站使用的推荐算法感兴趣，您可能需要研究一下协同过滤。这是一个非常简单的算法，而且非常有趣。
That's a great question!
Not all of the services you listed work the same way. Google does something called indexing, which is basically storing websites in a way where they can be looked up much more efficiently. Discord also does this with messages, which you may have noticed if you use it.
Netflix has about 2000 shows, whereas there are millions and millions of websites, and probably billions of Google-indexed pages. So doing a Netflix search is much simpler and probably doesn't require much indexing or fanciness.
If you're interested in the recommendation algorithms sites like Netflix and YouTube use, you might want to look into Collaborative Filtering. It's a pretty simple algorithm, and it's really interesting.