两台服务器，总价格2.8万。两路AMD Opteron 2GHz，4GB RAM，143G SCSI硬盘，一台做web，一台做DB。应该说我们代码写的还是挺烂的，性能不算好。目前每天PageView大概35-40万左右，每天Web Server端口流量大概15GB。Web Server的CPU负载在20~40%之间，DB Server的负载在20~35%之间，还是比较轻松的，预计可以支持到100万PageView。

当然，如果我们几个月以后整个代码重写，JavaEye3.0版本上线，我估计这硬件配置支持200万PageView应该没有问题。当然咯，如果JavaEye能够达到200万PageView，那已经是一个非常成功非常有钱的网站了，到那个时候，性能问题已经不用我操心了，哈哈。

哦，对了，补充一句，刚看到你说的是月PageView，一开始看成日Pageview 800万了。JavaEye如果算月PageView那应该有1000万了。

During browsing, the user's attention (and their money) is up for grabs. By showing
the user something compelling, a web site maximizes the likelihood of a transaction. So
if a web site can increase the chances of giving users good recommendations, it makes
more money. Obviously this is a difficult problem, but the incentive to solve it is very
big. The main approaches fall into the following categories:

• Personalized recommendation - recommend things based on the individual's past
  behavior



• Social recommendation - recommend things based on the past behavior of similar
  users



• Item recommendation - recommend things based on the thing itself



• A combination of the three approaches above