On Sat, Jan 25, 2014 at 3:51 PM, Tevfik Aytekin <tevfik.aytekin@gmail.com>wrote:
> Case 1 is fine, in case 2, I don't think that a dot product (without
> normalization) will yield a meaningful distance measure. Cosine
> distance or a Pearson correlation would be better. The situation is
> similar to Latent Semantic Indexing in which documents are represented
> by their low rank approximations and similarities between them (that
> is, approximations) are computed using cosine similarity.
> There is no need to make any normalization in case 1 since the values
> in the feature vectors are formed to approximate the rating values.
>
> That's exactly what I was thinking.
Thanks for your reply.
> On Sat, Jan 25, 2014 at 5:08 AM, Koobas <koobas@gmail.com> wrote:
> > A generic latent variable recommender question.
> > I passed the useritem matrix through a low rank approximation,
> > with either something like ALS or SVD, and now I have the feature
> > vectors for all users and all items.
> >
> > Case 1:
> > I want to recommend items to a user.
> > I compute a dot product of the user’s feature vector with all feature
> > vectors of all the items.
> > I eliminate the ones that the user already has, and find the largest
> value
> > among the others, right?
> >
> > Case 2:
> > I want to find similar items for an item.
> > Should I compute dot product of the item’s feature vector against feature
> > vectors of all the other items?
> > OR
> > Should I compute the ANGLE between each par of feature vectors?
> > I.e., compute the cosine similarity?
> > I.e., normalize the vectors before computing the dot products?
> >
> > If “yes” for case 2, is that something I should also do for case 1?
>
