我在哪里可以了解推荐系统? [英] Where can I learn about recommendation systems?

问题描述

我想尝试构建一个推荐系统，我的意思是一种算法，可以查看用户发布的偏好和/或评论，然后为他们提出建议，类似于 netflix 或亚马逊使用的算法.

学习如何编写这样的东西有哪些好的资源?我应该从哪里开始?

解决方案

在 上查看维基百科页面Netflix 奖及其讨论论坛.此外，有些相关的 2009 GitHub Contest 是许多不同推荐引擎上完整源代码的良好来源.很明显，还有 关于该主题本身的维基百科页面，其中有一些不错的链接.>

如果您开始自己编写，您将需要使用语料库.我实际上建议使用 Netflix Prize 的数据集.只需将数据集分成两部分.对第一部分进行训练，并在第二部分为您的算法评分.

附录:这类事情的一个有点相关和可怕的应用是 预测人口统计信息:用户的性别、年龄、家庭收入、智商、性取向等.这些属性中的大部分你都可以做到Netflix Prize 数据集具有相当高的准确性.幸运的是该数据集中的每个人都只是一个数字.

I'd like to play around with building a recommendations system, and by that I mean an algorithm that looks at preferences and/or reviews posted by a user and then makes recommendations for them, similar to what netflix or amazon use.

What are some good resources for learning how to write something like this? Where should I start?

解决方案

Check out the Wikipedia page on the Netflix Prize and its discussion forum. Also, the somewhat related 2009 GitHub Contest is a good source for full source code on a number of different recommendation engines. And obviously there's also the Wikipedia page on the topic itself, which has some decent links.

If you start writing your own, you'll want to use a corpus. I'd actually recommend using the Netflix Prize's data set. Just carve the data set into two pieces. Train on the first piece and score your algorithm on the second piece.

Addenda: A somewhat related and scary application of this sort of thing is predicting demographic information: a user's gender, age, household income, IQ, sexual orientation, etc. You could probably do most of these attributes with the Netflix Prize dataset with a fairly high degree of accuracy. Fortunately everyone in that dataset is just a number.

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