如何推荐系统的工作？ [英] How do recommendation systems work?

问题描述

我一直好奇的是，这些系统是如何工作的。例如，如何Netflix的或亚马逊决定什么建议，让基于过去的购买和/或收视率？是否有任何的算法来读了？

I've always been curious as to how these systems work. For example, how do netflix or Amazon determine what recommendations to make based on past purchases and/or ratings? Are there any algorithms to read up on?

正是这样有没有误解在这里，没有实际的原因，我问。我只是问了纯粹的好奇。

Just so there's no misperceptions here, there's no practical reason for me asking. I'm just asking out of sheer curiosity.

（另外，如果有关于这一主题的现有问题，点我吧。推荐系统是一个艰难的词来搜索。）

(Also, if there's an existing question on this topic, point me to it. "Recommendations system" is a difficult term to search for.)

推荐答案

这是 Netflix公司推出$ 1万美元奖金用于改善这样一个重要的商业应用程序他们的建议10％。

在几年人们越来越接近（我认为他们是高达9％左右，现在的），但它的打拼，原因是多方面的。也许最重要的因素还是在Netflix的奖最大的初步改进是使用了一个名为奇异值分解的统计方法

After a couple of years people are getting close (I think they're up around 9% now) but it's hard for many, many reasons. Probably the biggest factor or the biggest initial improvement in the Netflix Prize was the use of a statistical technique called singular value decomposition.

我强烈建议你读<一href="http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html?%5Fr=1&partner=permalink&exprod=permalink">If你喜欢这个，你一定会喜欢这的特别和推荐系统进行了深入的讨论Netflix的奖一般。

I highly recommend you read If You Liked This, You’re Sure to Love That for an in-depth discussion of the Netflix Prize in particular and recommendation systems in general.

基本上虽然亚马逊等的原理是相同的：他们寻找模式。如果有人买了星球大战三部曲以及有一个甚至比更好的机会，他们喜欢捉鬼者巴菲比普通客户（纯粹由例子）。

Basically though the principle of Amazon and so on is the same: they look for patterns. If someone bought the Star Wars Trilogy well there's a better than even chance they like Buffy the Vampire Slayer more than the average customer (purely made up example).

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