Well, I feel like I left some of you hanging (those who read my blog but don’t follow me on Twitter or Facebook or FriendFeed or … something) ...
Which is to say, I got the highest grade possible for my project (yes, RIT grades your Master’s project when you defend it) and am (unofficially, since I’m still waiting for the paperwork to clear) done with my Master’s degree.
Here are the slides from my presentation (not very exciting without all the stuff I said … maybe I should scan in my 3.5” cards), along with the source .tex including my final paper, the proposal and the original research I did. The source code for the prioject is available, as well as compiled binaries and if you actually want to work on it, I’ve got my database which I could make available.
I intended to spend last week celebrating my defense by releasing a new PoshCode build, but stuff happened™ and before I knew it, I had spent the whole weekend hanging out with my kids, and setting up a new MythTV box, and then upgrading my main development box to Vista 64bit (I’ve been frustrated about missing out on some of my RAM because I was running 32-bit Vista).
That’s pretty much completed now (except for the hanging out with the kids part
), so tonight, or tomorrow at the latest, I will get back to the work I had left for PoshCode and try to get that done in time to use it in my presentation next week.
And on top of that, I’ve got a ton of work at work now, mostly involving writing Ruby ...
Well, my project report has been accepted and I’m defending my Master’s on December 2nd, 2008 at 4:30pm. You’re all invited to come and see how slick my Self-Organizing Maps Recommender is, from database to PowerShell cmdlets and all.
This project comprises designing and implementing a hybrid recommender system for web–pages which uses data from a social tagging system to recommend interesting items to users. For this implementation, the tagging data comes from del.icio.us, the oldest and largest public social bookmarking or tagging system for web pages. The system clusters items using a pair of self–organizing maps (SOM) networks and allows users to see the system’s evaluation of their region of interest, or set their own regions.
The focus of this project was the web-scraper for gathering tagged URLs from del.icio.us, and the recommender system. The SOM networks are built using the GHSOM implementation, and several recommenders were built to compare results: one using a single map for URLs and users and the other using separate maps to compare the relative quality of the recommendations.