什么是好的算法暗示的东西，有人可能会喜欢根据自己的previous选择？ （例如，作为普及亚马逊建议书，并在服务，如愤怒的无线电或YAPE使用你在哪里得到（按排名）项建议）

What's a good algorithm for suggesting things that someone might like based on their previous choices? (e.g. as popularised by Amazon to suggest books, and used in services like iRate Radio or YAPE where you get suggestions by rating items)

推荐答案

简单明了（订购车）：

请奉命哪些项目合作方面事务的列表。例如，当有人买在亚马逊摄像机，他们也购买媒体记录在同一时间。

Keep a list of transactions in terms of what items were ordered together. For instance when someone buys a camcorder on Amazon, they also buy media for recording at the same time.

在决定一个给定的产品页面上什么是建议，看看所有产品是安全的次序排列，其中订单，算上所有的在同一时间购买的其他物品，然后显示前5项最经常购买同时

When deciding what is "suggested" on a given product page, look at all the orders where that product was ordered, count all the other items purchased at the same time, and then display the top 5 items that were most frequently purchased at the same time.

您可以从那里不仅基于订单展开它，但是在顺序是什么人搜索的网站等有关。

You can expand it from there based not only on orders, but what people searched for in sequence on the website, etc.

在评级系统而言（即电影评级）：

当你在收视率把它变得更加困难。而不是一个离散的篮子一个人购买的物品，你有项评级的客户历史记录。

It becomes more difficult when you throw in ratings. Rather than a discrete basket of items one has purchased, you have a customer history of item ratings.

在这一点上你看数据挖掘和复杂性是巨大的。

At that point you're looking at data mining, and the complexity is tremendous.

有一个简单的算法，但是，从上面的不远处，但它采用了不同的形式。以客户的最高级别的项目，和最低额定项，并查找等客户提供类似最高级别和最低额定列表。你想与其他人有类似的极端的好恶与它们匹配 - 如果你专注于喜欢而已，那么当你认为他们讨厌的东西，你必须给他们一个不愉快的经历。在建议系统中，你总是要犯错的冷淡的经验，而不是仇恨的一面，因为一个坏的经验，将使用该建议变味他们。

A simple algorithm, though, isn't far from the above, but it takes a different form. Take the customer's highest rated items, and the lowest rated items, and find other customers with similar highest rated and lowest rated lists. You want to match them with others that have similar extreme likes and dislikes - if you focus on likes only, then when you suggest something they hate, you'll have given them a bad experience. In suggestions systems you always want to err on the side of "lukewarm" experience rather than "hate" because one bad experience will sour them from using the suggestions.

推荐在其他的最高列表中的项目给客户。

Suggest items in other's highest lists to the customer.