MyFeedz is an interesting tool from Adobe Lab’s — from Adobe’s Romanian office, to be precise — that aspires to build a personalized news service based on your RSS reading habits.

MyFeedz has two ways to learn about your interests. One is by simply watching you read the titles and first paragraphs of text and noting when you click through to read the full text. The other involves jump-starting the system (at your choice) by uploading your RSS reading list via an OPML file (OPML files can be output from most popular RSS aggregators, including Bloglines and Google Reader). The process behind the scenes is not quick — I suggest getting a cup of coffee, walking the dog, or reading your day’s RSS feeds while MyFeedz works.

When MyFeedz is done doing its magic — described somewhat cryptically on the site as a process involving an analysis of “its source, tags, popularity, rating, language and more” — it has built a profile of the subjects that you generally prefer to read. It then goes out and looks for more blog entries that match this general profile.

Here is where I’ll speculate a bit on what is going on behind the scenes. My guess is that MyFeedz is using some form of vector analysis to model the items in which you have shown an interest. Vector analysis (see this Wikipedia article for more of the math than I understand) compares texts and scores them with a probability that they are about the same thing, even if the same exact phrases do not appear in both documents.

If I am correct, that is the time-consuming part. For my aggregated list, MyFeedz took several hours to be ready to show me new articles based on the profile it generated for me. But now, it is showing me articles within the sphere of what I’ve already expressed interest in knowing about. As I write this, three of its top five recommendations for me are:

The other two are less clearly germane (one about the Dodgers and I’m a member of Red Sox Nation, the other about NJ Governor Corzine’s political difficulties). I think batting .600 is pretty good for an automated recommendation engine that I just recently started using. However, as I’ve written before (see “Serendipity at Risk“), I like a bit of surprise in my reading, as long as it is tangentially related to what I’m focusing on. The items I catch out of the corner of my eye while I’m looking in one direction are often fascinating.

I will be curious to see if, as I work with MyFeedz, if it continues to narrow in on my core interests while providing me some “ah-ha!” moments. MyFeedz will not replace my aggregator — that is not its purpose — but it makes for an interesting discovery tool.