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).