Hi Ted, Could you explain what do you mean by a "dithering step" and an "anti-flood step"? By dithering I guess you mean adding some sort of noise in order not to show the same results every time. But I have no clue about the anti-flood step.
Tevfik On Sat, Jan 25, 2014 at 11:05 PM, Koobas <koo...@gmail.com> wrote: > 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? >>
