Re: generic latent variable recommender question

On Sun, Jan 26, 2014 at 9:36 AM, Pat Ferrel p...@occamsmachete.com wrote: I think I’ll leave dithering out until it goes live because it would seem to make the eyeball test easier. I doubt all these experiments will survive. With anti-flood if you turn the epsilon parameter to 1 (makes

Re: generic latent variable recommender question

Thanks for the answers, actually I worked on a similar issue, increasing the diversity of top-N lists (http://link.springer.com/article/10.1007%2Fs10844-013-0252-9). Clustering-based approaches produce good results and they are very fast compared to some optimization based techniques. Also it

Re: generic latent variable recommender question

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

Re: generic latent variable recommender question

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

Re: generic latent variable recommender question

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

Re: generic latent variable recommender question

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

Re: generic latent variable recommender question

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

Re: generic latent variable recommender question

On Sat, Jan 25, 2014 at 4:33 PM, Pat Ferrel p...@occamsmachete.com wrote: BTW can you explain your notation? s = log r + N(0,log \epsilon) N?, \epsilon? r is rank N is normal distribution \epsilon is an arbitrary constant that drives the amount of mixing. Typical values are =4.

Re: generic latent variable recommender question

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

generic latent variable recommender question

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

Re: generic latent variable recommender question

Case 1 is fine as is. For Case 2 I would suggest to simply experiment, try different similarity measures like euclidean distance or cosine and see what gives the best results. --sebastian On 01/25/2014 04:08 AM, Koobas wrote: A generic latent variable recommender question. I passed

Re: generic latent variable recommender question

On Fri, Jan 24, 2014 at 7:08 PM, Koobas koo...@gmail.com wrote: I eliminate the ones that the user already has, and find the largest value among the others, right? yeah... Unless you are selling razor blades in which case, you don't eliminate repeats. Also, you may want to pass the results