Thanks to everyone for their comments; very helpful indeed. One question/observation on LogLikelihood similarity, if I'm right this metric does not take into account preference values so I would just be looking at what users have clicked on, not how many times they have clicked. However, since I have no idea how significant extra clicks are I think this is a good one to start with.
With more work I'll try to evaluate the quality of that measure (number of clicks) and look for others that could be better indications of preference. Cheers, Simon Reavely On Wed, Jul 28, 2010 at 12:53 PM, Ted Dunning <[email protected]> wrote: > The position of users and items in the user x item occurrence matrix are > interchangeable. For every solution problem that matches items to users, > there is a dual solution that matches users to items. > > In any case, log-likelihood methods in recommendation usually are applied > to > the item cooccurrence matrix to get an item-based recommendation algorithm. > > On Wed, Jul 28, 2010 at 9:49 AM, Tanton Gibbs <[email protected] > >wrote: > > > Very cool, I didn't realize it handled item similarity alongside user > > similarity. > > > -- Simon Reavely [email protected]
