Hello, I am doing my bachelor thesis and I have a question about non-negative matrix factorization.
I am trying to use NNMF for a recommender system, but I am not sure how to make recommendations for new users. As an example, for every user there is a row, and the columns are the available videos. If the user watched a video, the corresponding column has a 1 in it, if not there is a 0. There are no user ratings. This matrix V gets factorized into W and H (V= W*H + Error). How can I use W and H to make recommendations for new users? Let´s say I already know some videos that the user watched. Do I have to add this user as a new row to V, and then re-run the NNMF algorithm? With the new W' and H' matrices, I could approximate V' = W' * H', and in the row of the new user, there should be videos that could be recommended. Do I understand this right? And is there a better way to do this than to re-run NNMF for every new user? (Another approach that I thought could work, if I compare the new-user-row with the feature matrix H, I could search for features that share similarities with the new-user-row.) With best regards, Jan -- View this message in context: http://lucene.472066.n3.nabble.com/NNMF-Recommendations-tp3990505.html Sent from the Mahout User List mailing list archive at Nabble.com.
