I have always defined popularity as just the number of ratings/prefs, yes. You could rank on some kind of 'net promoter score' -- good ratings minus bad ratings -- though that becomes more like 'most liked'.
How do you get popularity from similarity -- similarity to what? Ranking by sum of similarities seems more like a measure of how much the item is the 'centroid' of all items. Not necessarily most popular but 'least eccentric'. On Thu, Feb 6, 2014 at 7:41 AM, Tevfik Aytekin <[email protected]> wrote: > Well, I think what you are suggesting is to define popularity as being > similar to other items. So in this way most popular items will be > those which are most similar to all other items, like the centroids in > K-means. > > I would first check the correlation between this definition and the > standard one (that is, the definition of popularity as having the > highest number of ratings). But my intuition is that they are > different things. For example. an item might lie at the center in the > similarity space but it might not be a popular item. However, there > might still be some correlation, it would be interesting to check it. > > hope it helps > > > > > On Wed, Feb 5, 2014 at 3:27 AM, Pat Ferrel <[email protected]> wrote: >> 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 item-item similarity >> matrix. It doesn't need to be transposed and contains the "important" >> similarities--as calculated by LLR for example. Here similarity means >> similarity in which users preferred an item. So summing the non-zero 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 user-item matrix seems >> to yield a fromat that cannot be sent directly into clustering.
