Video: How Reinforcement Learning Enables Personalized Viewing Experiences
了解更多关于个性化的信息 流媒体的下一个事件.
阅读这段录音的完整文本:
Rafah Hosn: 这里有另一个可以考虑的解决方案. 这是机器学习的一个新范例,叫做强化学习. 在这里,我们使用真实的终端用户反馈来训练我们的模型. So, how does that work? 让我们再看一下新闻的例子.
We have a user. 他们来到他们最喜欢的新闻网站. 在后台有一个在线学习软件. 无论何时用户进来,用户都有上下文. 这意味着它们有一些特性集, 他们所在的地理位置, 或者他们使用的设备类型. 我们称它为上下文X. Then, inside that red box, there is a policy, a model, that's choosing the best list of new stories for this user based on that context X. We call this an action.
强化学习的关键在于我们不会就此止步. 学习者提出一个动作,然后等待用户的反馈. 这就是为什么它被称为强化学习, 因为每次用户点击, 这对在线学习者来说是一种正强化反馈. 这就像教小狗玩把戏一样. 每次小狗做点什么,你就给它一点奖励. 这是一个积极的反馈. 每次小狗做错事,你就说:“坏小狗。.“它不会从中吸取教训. 原理是一样的. 这就是我们用于个性化的范例.
现在,强化学习的关键是一个叫做探索的概念. 给那位问起猫和狗的先生, 至少在我们的强化学习中是这样, 假设你喜欢太空. So in most, 80% of the time we're going to choose for you space articles because that what we learn that you like. But add some random .2%, .1%, 2%,我们选一篇不同的文章. So, we'll show you cats and at some configurable number, we say, "You know what? We're going to explore this space and see if this gentleman actually likes a little bit of dogs.“所以,我们要让狗看看. 然后我们观察你对狗的反应. 如果你给我们一个积极的反馈,我们会说,“哦,好吧. Okay. 也许不仅仅是猫. 也许他真的有点喜欢狗."
这种探索是非常非常强大的. 这不是完全的随机探索. 它是对一系列可行行动的探索. 所以在新闻的背景下, 你的社论有12个故事列表, 应该在页面上显示的热门故事. So, 80%的时间我们会播放我们认为你喜欢的新闻, and 20% we will randomize over this list of 12 articles and propose a different type of article and see if the user reacts positively to it. 这就是这个算法的正强化反馈.
It turns out that exploration is so powerful because it allows you to now label your data set automatically. You don't need to go and spend money labeling your data set because every positive reinforcement is a label. 任何时候你在探索,你实际上是在增加你的数据集. 所以,这给了你一个非常丰富的数据集,你可以从中学习.
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