Thank you for that suggestion. I have a few different actions that users can do.. "view", "add to cart", and "buy" which I've assigned different preference values to. Perhaps it would be better to simply use boolean yes/no in my case?
I'll give the log likelihood stuff a try tonight and I'll report back in case anyone else runs into this issue. -Matthew Runo On Wed, Feb 16, 2011 at 2:31 PM, Chris Schilling <[email protected]> wrote: > Mathew, > > I was running into a similar issue with my data. I discussed it with Sean > Owen offline and his advice was, in a nutshell, to use the log-likelihood > similarity metric. Since you describe your users as having only links, I > assume you are not dealing with preference data. So, with the boolean data, > the log-likelihood metric works very well (in my case, which I am also > dealing with very sparse data). How do your results look if you try the > likelihood approach? > > Hope this helps, > Chris > > > On Feb 16, 2011, at 2:24 PM, Matthew Runo wrote: > >> Hello folks - >> >> (I think that) I'm running into an issue with my user data being too >> sparse with my item-item similarity calculations. A typical item_id in >> my data might have about 2000 links to other items, but very few >> "combinations" of users have viewed the same products. >> >> For example we have two items, 1244 and 2319 - and there are only >> three users in common between them. >> >> So, there's only those three users who viewed both items. I'm >> assigning preferences to different types of actions in my data.. and >> since all three users did the same action towards the item, they have >> the same preference value. Maybe I just need to start with a bigger >> set of data to get more links between items in different "actions" in >> order to spread out the generated similarities? I'm using the >> EuclideanDistanceSimilarity to do the final computation. >> >> I think this is leading to a huge number of "1" values being returned. >> Nearly 72% of my item-item similarities are 1.0. I feel that this is >> invalid, but I'm not quite sure of the best way to attack it. >> >> There are some similarities of 1 where the items do not appear to be >> similar at all, and the best I've been able to come up with as to how >> the 1 came around was that there was only one user who had a link >> between them and so that one user. >> >> How many item-user-item combinations per item pair does it take to get >> good output? >> >> Sorry if I'm not quite describing my problem in the proper terms.. >> >> --Matthew Runo > >
