Plugin gives recommendations based on posts user have seen on your blog.

This plugin finds and suggests relevant, potentially interesting content from 
your blog to visitors. The goal is to push the relevant content to the visitor 
with the prospect of increase of page views on your blog and eventually 
satisfied and returning visitor.
Plugin is able to recommend similar posts and pages using two different 
approaches. The first is content based and produces list of the most similar 
items based on words occurring in both items. Advantage of this approach lies 
in is its simplicity for users to install and it works straight from the start 
(there’s no so-called “cold start” period from the collaborative filtering 
approach).
The second approach is employing collaborative filtering to identify post and 
pages that might interest user. Suggested items are calculated from the users’ 
browsing history that plugin logs on the aliiike server. Server accepts such 
logs only if you are registered and you created an account. Disadvantage of 
this approach is the fact that you need to wait until enough data is logged. 
If the traffic on your site is low it can last for a while. Advantage of this 
approach is the fact that you are modeling users’ behaviour (rather than the 
contents), which in normal circumstances should easily outperform the content 
based approach.
Important aspect of plugin is its ability to serve recommendation lists to a 
settable percentage of users only. In combination with the Google Analytics 
this feature allows you to perform so-called A/B testing to measure the 
influence of the recommended items on your visitors’ browsing behavior (or 
sales increase or decrease in case you are running a web shop).
See the <a href="http://aliiike.com/wordpress/" title="plugin homepage" rel="nofollow ugc">Aliiike recommender homepage</a> 
as well as the rest of the <a href="http://aliiike.com" rel="nofollow ugc">site</a> for more information.

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