视频类
Netflix：很多方法的融合，可以参考blog。
Hulu：主要是item based CF，参考官方博客上的文章。
Youtube：开始是random walk，后来改为类似item based CF的方法，这是他们算法团队发表的论文。recsyschina上的点评， 豆瓣阿稳的点评。
图书类
Amazon：好多方法都用了，主要是 item based CF，参考Greg Linden的论文。这里讨论了一下其推荐系统的有效性；另外，这篇博客有一个简要的讨论，包含pandora的推荐方法。
资讯类
google news：用了CF和bayesian的方法：CF论文，bayesian论文。
digg：算法是 热门度+topic driven user based CF，官方博客找不到了，算法说明在这里。项亮的点评。
zite：可以参考这里。
音乐类
last.fm：用的是CF，参考这里。
yahoo music：参考Koren的论文。
pandora：音乐基因项目，主要依赖专家标注。参考这里，和这里，以及一篇对比的文章。
社交类
facebook：算法叫Edgerank， 参考这里。
twitter：主要场景是推荐其它用户，参考官方介绍，以及这里。
linkedin：负责人是Christian Posse，他在kdd2012上的slides。或者参考他们刚发表的论文，以及这里。
综合推荐
stumbleupon: 参考这里，网页上的图打不开了，放在下面。一篇综述的文章。
最后，以下是几篇不错的关于推荐系统的文章。
比较杂的：相关性时代，推荐系统的机会，推荐系统的挑战，无处不在的推荐，推荐系统微观点
豆瓣阿稳的：推荐算法与推荐产品，集体智慧vs专家智慧，推荐算法vs过滤系统
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