We have a number of options, with SVM being the fastest so far b/c the library is written in C. Pearson, NaiveBayes and a number of others have all been tried a

We have a number of options, with SVM being the fastest so far b/c the library is written in C. Pearson, NaiveBayes and a number of others have all been tried and "tested" for acceptance with several thousand users and the majority picked the results from SVM as the most accurate. The recs are computed offline but there is an option to do that on the fly given enough RAM to load the SVM models for all users. As far as asking users for too much - what exactly do you mean? All we are asking for is for the user to login and if they find article they like (on the front page or from the topics menu) then click on them. What more simple that that? The voting system up/down you talk about is exactly the same except that you explicitly ask the user to vote, while we implicitly do the same. TechnologyReview ran an article last year how asking the user to explicitly rate stuff is considered overburdening and how the system should quietly monitor the user without asking direct questions. Anyways, your up/down approach and our click/remove approach are the same in my opinion. But thanks for the comments.