3 ms·

1) The common KNN item-item approach doesn't handle negative correlations. Er, why not?

• nkurz 16y ago
  I may have been too aggressive trying to shorten that statement down to one line. What I meant was that item-item approaches, when used as recommenders, won't make recommendations of the 'users who hated X also liked Y' variety, and instead limit their recommendations to items that correlate positively with the user's favorites. The usual approach for item-item is to precalculate similarities for all items in the set, using Pearsons correlation or cosine. There's no problem with negative correlations being used to make predictions, the problem comes when you try figure out what to recommend. The recommendation step consists of looking at the items a user has already rated highly, and then looking up which items have a strong positive correlation to these, then determining which of these to put at the top of the list. Items with negative correlations will thus never be offered. There's nothing to prevent one from throwing in anti-correlations of hated movies, but I've never heard of it being done. There's also nothing wrong with making predictions for every movie in the set and then choosing the highest (taking full account of negative correlations) but this is generally thought to be too computationally expensive.