Ask HN: Comments on and help on improving our recommendation engine for news
A friend of mine and I built this news ranking service of sorts based on a ranking algorithm I came up while I was in grad school for CS. Recently I also developed a recommendation engine based on a modified Support Vector Machine (SVM) algorithm. Why modified? Well, as most of you know SVM are really binary classifiers and need to classes of training data - i.e. good/bad. interesting/boring, etc. So in the case of news, you have to ask the user to select both interesting articles and not interesting articles. There have been a number of studies done on the subject how asking the user to do both is soometimes too much of a burden. Ideally, the user should tracked implicitly and the model should account for the uncertainty that arises from lack of explicit ratings. So here is how it works. I created an account and seeded the system with some articles that seem to have been popular on HN and the results are available. Just go to http://www.euraeka.com and click login on the top right. The user name is hackers (at) are (dot) us, and you can email me at haidut (at) gmail (dot) com for the password. Since the system already generated recommendations, you will see them right when you login. If a user likes an article they just click on it and the system records that. There is also a "remove" button next to each article in case the user knows up front they don't like that article and want it removed from view and recorded as uninteresting. The purpose of the remove option is also if you clicked on an article that you thought you will like but it turned out to be not interesting, so you have the option of reversing your initial decision and telling the system that the article was not good. Like I said above, "removing" an article is optional. The system can work if the user only clicks on articles that he/she likes and ignores the rest. Note: Obviously, I couldn't seed the system with every article that has been on HN. I just picked some that I liked before from topics such as science, entrepreneurship, health, etc. Feel free to seed the system even more. How to do that? Basically, once you are logged in either use the search box at the top to search for keywords of interest, or use the topics section at the right of the screen to find more specific news. If you find anything you like just click on the article. Note: Obviously, if you create your own account you will be able to test your own set of recommendations. Recommendations are generated every 24 hours using ALL the news collected in the last 24 hours. If any of you have used the Google News or Digg recommendaton engines, I'd very interested to hear how this stacks up against those to services, given the fact that they are both based on user-to-user recommendation techniques (i.e. articles are recommended to you based on similarity to other users) rather than content-based recommendations (which is what Euraeka is).
Thanks in advance.