On Sat, Jan 25, 2014 at 3:51 PM, Tevfik Aytekin <tevfik.ayte...@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 <koo...@gmail.com> wrote: > > A generic latent variable recommender question. > > I passed the user-item 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? >
