Rising popularity is often a better match to what people want to see on a "most popular" page.
The best measure for that in my experience is log (new_count + offset) / (old_count + offset) where new and old counts are the number of views during the periods in question and offset is used partly to avoid log(0) or x/0 problems, but also to give a Bayesian grounding to the measure. On Thu, Feb 6, 2014 at 5:33 PM, Sean Owen <[email protected]> wrote: > Agree - I thought by asking for most popular you meant to look for apple > pie. > > Agree with you and Ted that the sum of similarity says something > interesting even if it is not popularity exactly. > On Feb 6, 2014 11:16 AM, "Pat Ferrel" <[email protected]> wrote: > > > The problem with the usual preference count is that big hit items can be > > overwhelmingly popular. If you want to know which ones the most people > saw > > and are likely to have an opinion about then this seems a good measure. > But > > these hugely popular items may not differentiate taste. > > > > So we calculate the “important” taste indicators with LLR. The benefit of > > the similarity matrix is that it attempts to model the “important” > > cooccurrences. > > > > There is an affect of hugely popular items where they really say nothing > > about similarity of taste. Everyone likes motherhood and Apple pie so it > > doesn’t say much about us if we both do to. This is usually accounted for > > with something like TFIDF so I suppose another weighted popularity > measure > > would be to run the preference matrix through TFIDF to de-weight > > non-differentiating preferences. > > > > On Feb 6, 2014, at 7:14 AM, Ted Dunning <[email protected]> wrote: > > > > If you look at the indicator matrix (cooccurrence reduced by LLR), you > will > > usually have asymmetry due to limitations on the number of indicators per > > row. > > > > This will give you some interesting results when you look at the column > > sums. I wouldn't call it popularity, but it is an interesting measure. > > > > > > > > On Thu, Feb 6, 2014 at 2:15 PM, Sean Owen <[email protected]> wrote: > > > > > 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. > > > > > > > >
