Hi, I'm exploring Mahout SVD parallel implementation over Hadoop (ALS), and I would like to clarify a few things : 1. How do you recommend top K items with this job? Does the job factorize the ranking matrix, than compute a predicted ranking for each cell in the matrix, so when you need a recommendation you only need to retrieve the top K items according to prediction value for the user? Or is it factorize the matrix and require some online logic when the recommendation is being asked? 2. From my knowledge, applying a SVD technique require first to fill in all empty cells in the ranking matrix (with average ranking for example). Is it something done during the ALS job (and if so, what is the way it's being filled), or should it be done as a preprocessing step? 3. From my understanding SVD recommenders are used to predict user implicit preference. By doing so you can recommend top K items (top K items over descending orders according to the prediction). I wonder, could it be applied on a binary dataset (explicit), where my ranking matrix contain only 1\0? 4. From doing some readings I found that the timeSVD++ developed by Yehuda Koren is considered as the superior SVD implementation for SVD recommenders. I wondered if there is any kind of a parallel implementation of it on top of Hadoop? I found this proposal: https://issues.apache.org/jira/browse/MAHOUT-371 I wonder, what is the status of it? Was it being checked already? Is it stable? Did anyone experienced with it?
Thanks, Oren --------------------------------------------------------------------- Intel Electronics Ltd. This e-mail and any attachments may contain confidential material for the sole use of the intended recipient(s). Any review or distribution by others is strictly prohibited. If you are not the intended recipient, please contact the sender and delete all copies.
