Heh you're reading my mind. I tried the cosine similarity and had exactly the problem with sparse rating recommendations that you mentioned. I'm switching back to the boolean data set and just having a minimum action threshold to cross, and I was just in the process of moving my logic around to handle negative actions as a filter.
Thanks for the quick responses! -Will On Sun, May 6, 2012 at 3:53 PM, Ted Dunning <[email protected]> wrote: > As Sean points out, cosine should pick up on this. You will have the usual > problems with small counts that any rating based system has. > > And in spite of your last comment, I would strongly recommend that you test > a boolean approach where in *any* action is considered positive and another > where you consider only your positive actions and ignore your negative > actions. If necessary, consider the negative actions at the presentation > tier. > > On Sun, May 6, 2012 at 10:48 AM, Will C <[email protected]> wrote: > > > So I've taken another try at using recommendations values. However, > unlike > > something that a user is explicitly rating on a scale of 0-5. I am using > a > > user's activity. Certain activities of a user toward an item are > negative, > > and certain are positive. > > > > If I have users 1 and 2 and 3, and product X, and their preferences are > as > > follows: > > > > 1, X, -1 > > 2, X, 1 > > 3, X, 10 > > > > Clearly 2 and 3 are closer than 2 and 1, because they both like product > X, > > just to varying degrees. However, most distance algorithms I've tried > are > > incorrectly showing 1 and 2 closer because their difference is less. > > > > Am I approaching this wrong? Other than switching to boolean > preferences, > > is there a better way to approach this? > > >
