Trying to come up with a relative measure of popularity for items in a recommender. Something
that could be used to rank items.
The user  item preference matrix would be the obvious thought. Just add the number of preferences
per item. Maybe transpose the preference matrix (the temp DRM created by the recommender),
then for each row vector (now that a row = item) grab the number of non zero preferences.
This corresponds to the number of preferences, and would give one measure of popularity. In
the case where the items are not boolean you’d sum the weights.
However it might be a better idea to look at the itemitem similarity matrix. It doesn’t
need to be transposed and contains the “important” similaritiesas calculated by LLR
for example. Here similarity means similarity in which users preferred an item. So summing
the nonzero weights would give perhaps an even better relative “popularity” measure.
For the same reason clustering the similarity matrix would yield “important” clusters.
Anyone have intuition about this?
I started to think about this because transposing the useritem matrix seems to yield a fromat
that cannot be sent directly into clustering.
