On Sat, Feb 2, 2013 at 1:03 PM, Pat Ferrel <[email protected]> wrote:
> Indeed, please elaborate. Not sure what you mean by "this is an important > effect" > > Do you disagree with what I said re temporal decay? > No. I agree with it. Human relatedness decays much more quickly than item popularity. I was extending this. Down-sampling should make use of this observation to try to preserve time coincidence in the resulting dataset. > As to downsampling or rather reweighting outliers in popular items and/or > active users--It's another interesting question. Does the fact that we both > like puppies and motherhood make us in any real way similar? I'm quite > interested in ways to account for this. I've seen what is done to normalize > ratings from different users based on whether they tend to rate high or > low. I'm interested in any papers talking about the super active user or > super popular items. > I view downsampling as a necessary evil when using cooccurrence based algorithms. This only applies to prolific users. For items, I tend to use simple IDF weightings. This gives very low weights to ubiquitous preferences. > > Another subject of interest is the question; is it possible to create a > blend of recommenders based on their performance on long tail items. Absolutely this is possible and it is a great thing to do. Ensembles are all the fashion rage and for good reason. See all the top players in the Netflix challenge. > For instance if the precision of a recommender (just considering the > item-item similarity for the present) as a function of item popularity > decreases towards the long tail, is it possible that one type of > recommender does better than another--do the distributions cross? This > would suggest a blending strategy based on how far out the long tail you > are when calculating similar items. Yeah... but you can't tell very well due to the low counts.
