blocks|key|1563579|text|亚马逊和Netflix+(以及其他公司)使用一种名为Collaborative+filtering的技术，根据与你相似的购买和选择的其他人的好恶，向你推荐你可能喜欢的东西。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1563580|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Collaborative_filtering^0|Q|N|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@$A|M|B|N|1|O]]|C|$]]|$1|D|3|-4|5|6|7|P|8|@]|9|@]|C|$]]]|E|$F|$5|G|H|I|C|$J|K]]]]

Amazon and Netflix (among others) use a technique called <a href="http://en.wikipedia.org/wiki/Collaborative_filtering" rel="nofollow noreferrer">Collaborative filtering</a> to suggest things you might like based on the likes/dislikes of others who have made purchases and selections similar to yours.

blocks|key|2596164|text|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2596165|有什么算法可以做到这一点吗？|blockquote|2596166|2596167|2596168|是|2596169|entityMap^0|0|0|0|0|0^^$0|@$1|2|3|-4|4|5|6|J|7|@]|8|@]|9|$]]|$1|A|3|B|4|C|6|K|7|@]|8|@]|9|$]]|$1|D|3|-4|4|5|6|L|7|@]|8|@]|9|$]]|$1|E|3|-4|4|5|6|M|7|@]|8|@]|9|$]]|$1|F|3|G|4|5|6|N|7|@]|8|@]|9|$]]|$1|H|3|-4|4|5|6|O|7|@]|8|@]|9|$]]]|I|$]]

<blockquote>
 Is there any Algorithm to do so?
</blockquote>

Yes

blocks|key|4004356|text|是。一个相当常见的方法是查看你过去选择的东西，找到其他做出这些选择的人，然后找到这些人中最常见的其他选择，然后猜测你可能也会对这些感兴趣。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4004357|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Yes. One fairly common one is to look at things you've selected in the past, find other people who've made those selections, then find the other selections most common among those other people, and guess that you're likely to be interested in those as well.

blocks|key|1559938|text|是的，有很多算法。例如k-最近邻居：http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1559939|这里有一本关于这个主题的很好的书，它涵盖了制作这些类型的系统和其他系统：http://www.amazon.com/gp/product/0596529325?ie=UTF8&tag=ianburriscom-20&linkCode=as2&camp=1789&creative=9325&creativeASIN=0596529325。|1559940|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm|1|https://rads.stackoverflow.com/amzn/click/com/0596529325^0|I|1L|0|0|10|3O|1|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@$A|U|B|V|1|W]]|C|$]]|$1|F|3|-4|5|6|7|X|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]|N|$5|I|J|K|C|$L|O]]]]

Yup there are lots of algorithms. Things such as k-nearest neighbor: <a href="http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm" rel="nofollow noreferrer">http://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm</a>.

Here is a pretty good book on the subject that covers making these sorts of systems along with others: <a href="https://rads.stackoverflow.com/amzn/click/com/0596529325" rel="nofollow noreferrer" rel="nofollow noreferrer">http://www.amazon.com/gp/product/0596529325?ie=UTF8&amp;tag=ianburriscom-20&amp;linkCode=as2&amp;camp=1789&amp;creative=9325&amp;creativeASIN=0596529325</a>.

blocks|key|1563525|text|它通常是通过将你与其他具有相似使用历史/个人资料的用户相匹配，然后推荐他们已经购买/观看/任何东西的其他内容。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1563526|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

It's generally done by matching you with other users who have similar usage history / profile and then recommending other things that they've purhased/watched/whatever.

blocks|key|1559889|text|搜索“推荐算法”会产生大量的论文。大多数算法都包含“机器学习”算法来确定事物组(喜剧电影、园艺书籍、管弦乐等)。您与这些组的匹配会产生推荐。一些公司也使用人类来对事物进行分类。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1559890|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Searching for "recommendation algorithm" yields lots of papers. Most algorithms incorporate "machine learning" algorithms to determine groups of things (comedy movies, books on gardening, orchestral music, etc.). Your matching with those groups yields recommendations. Some companies use humans to classify things, too.

blocks|key|2596291|text|这样的算法在不同的公司会有很大的不同。在许多情况下，它会分析你的搜索历史，购买历史，物理位置和其他因素的一些组合。它可能还会比较其他人的购买/搜索，以找出这些人购买/搜索的内容，并向您推荐其中的一些产品。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2596292|可能有数百种这样的算法，但我怀疑你是否能使用它们中的任何一种(这实际上是好的)。也许你自己弄清楚会更好。|2596293|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

Such an algorithm is going to vary wildly from company to company. In many cases, it analyzes some combination of your search history, purchase history, physical location, and other factors. It probably will also compare purchases/searches amongst other people to find what those people have purchased/searched for, and recommend some of those products to you.

There are probably hundreds of these algorithms out there, but I doubt you can use any of them (that are actually good). Probably you are better off figuring it out yourself.

blocks|key|817320|text|如果您可以对内容进行分类(即通过标记或内容分析)，则还可以对用户及其偏好进行分类。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|817321|例如:您有一个拥有500万个视频的视频门户网站。其中1MIO被标记为mostly+red。如果用户观看了80%25的视频(由IP定义，持久化用户账号...)都被标记为mostly+red，你可能想向他推荐更多的红色视频。你可能想通过观察他的进一步行动来完善你的建议:他喜欢你的建议吗--如果是，为什么不给他更多的建议，如果不是，试试第二个最好的猜测，也许他不是在寻找颜色，而是为了背景音乐……|offset|length|style|CODE|817322|没有绝对的算法可以做到这一点，但所有的实现都将朝着类似的方向进行。它总是建立在观察用户的基础上，这让我不时感到害怕:-)|817323|entityMap^0|0|Y|A|29|A|0|0^^$0|@$1|2|3|4|5|6|7|L|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|M|8|@$D|N|E|O|F|G]|$D|P|E|Q|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|R|8|@]|9|@]|A|$]]|$1|J|3|-4|5|6|7|S|8|@]|9|@]|A|$]]]|K|$]]

If you can categorize your contents (i.e. by tagging or content analysis), you can also categorize your users and their preferences.

For example: you have a video portal with 5 million videos .. 1 mio of them are tagged <code>mostly red</code>. If 80% of all videos watched by a user (who is defined by an IP, a persistent user account, ...) are tagged <code>mostly red</code>, you might want to recommend even more red videos to him. You might want to refine your recommendations by looking at his further actions: does he like your recommendations -- if so, why not give him even more, if not, try the second-best guess, maybe he's not looking for color, but for the background music ...

There's no absolute algorithm to do it, but all implementations will go into a similar direction. It's always basing on observing users, which scares me from time to time :-)

blocks|key|2596438|text|有很多算法可以解决这个问题：Wiki+article。这是一个机器学习领域的问题。可以使用两种主要技术来学习计算机:分类和聚类。它们需要一些数据集作为输入。如果数据集是信息性的(确实包含一些有用的模式)，那么这些ML技术就可以挖掘其中的大部分。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2596439|集群可能是解决这类问题的最佳选择。它的主要用途是在提供的数据集中找到点之间的相似性。例如，如果这些点是您的搜索历史记录，则可以将它们组合在一起以形成特定的集群。如果你的搜索历史记录与另一个密切相关，可以给出一个提示--选择与你的最相似的链接。|2596440|图书推荐也是如此--很明显，他们使用的是什么数据集：“其他购买该产品的人也购买了产品A、产品B……”。这里的关键是将你的个人资料与其他人的相匹配，并使用最相似的来推荐。|2596441|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Recommender_system^0|E|C|0|0|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@]|9|@$A|Q|B|R|1|S]]|C|$]]|$1|D|3|E|5|6|7|T|8|@]|9|@]|C|$]]|$1|F|3|G|5|6|7|U|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|V|8|@]|9|@]|C|$]]]|I|$J|$5|K|L|M|C|$N|O]]]]

There's whole lot of algorithms tackling the issue: <a href="http://en.wikipedia.org/wiki/Recommender_system" rel="nofollow noreferrer">Wiki article</a>. It's a Machine Learning domain problem. Computer's can be learned using two main techniques: classification and clustering. They require some datasets as input. If the dataset is informative (really holds some useful patterns) than those ML techniques can dig most of it.

Clustering could be best to use for this kind of problem. It's main usage is to find similarities among points in provided dataset. If the points are, e.g. your search history, they can be grouped together to form certain clusters. If Your search history closely relates to another, a hint can be given - picking links that are most similar to Your's.

The same comes with book recommendations - it's obvious what dataset they use: "Other people who bought this product also bought Product A, Product B,...". The key here is to match your profile to other's and use the most similar to recommend.

blocks|key|4004688|text|计算机通过复杂的记忆扫描过程从人脑中检索信息，并根据到目前为止你在生活中所经历的事情对其进行排序并输出结果。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|4004689|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

The computer retrieves information from the human brain with complex memory scan process, sorts it accordingly and outputs results based on what you have experienced in your life so far.

Recently, I found several web site have something like : "Recommended for You", for example youtube, or facebook, the web site can study my using behavior, and recommend some content for me... ...I would like to know how they analysis this information? Is there any Algorithm to do so? Thank you.

How the computer knows "Recommended for You"?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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最近，我发现几个网站都有这样的内容：“推荐给你”，比如youtube，或者facebook，这些网站可以研究我的使用行为，并为我推荐一些内容……...I想知道他们是如何分析这些信息的？有什么算法可以做到这一点吗？谢谢。

问电脑怎么知道“推荐给你”？
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