After thinking about it more, I think your theory is right. You really should use more like 90% of your data to train, and 10% to test, rather than the other way around. Here it seems fairly clear that the 10% training test is returning a result that isn't representative of the real performance. That's how I'd really "fix" this, plain and simple.
Sean On Wed, Jan 4, 2012 at 11:42 AM, Nick Jordan <[email protected]> wrote: > Yeah, I'm a little perplexed. By low-rank items I mean items that have a > low number of preferences not a low average preference. Basically if we > don't have some level of confidence in our ItemSimilarity based on the fact > that not many people have given a preference good or bad, don't recommend > them. To your point though LogLikelihood may already account for that > making these results even more surprising. > >
