I replied on stackoverflow. Did you translate your ids into mahout ids? Mahout ids must be ordinal integers for users and items. You will need to translate into mahout ids before the data is prepared correctly and translate into your application specific ids when reading the output. I updated the page you referenced to note this but it’s just a guess.
Can you share a few lines of your input? On Jul 7, 2014, at 11:29 AM, Sneha Venkatesh <sneh...@gmail.com> wrote: Hi, I am new to mahout and I building an implicit feedback recommender using the parallelALS job given here <https://mahout.apache.org/users/recommender/intro-als-hadoop.html>. Each row of my dataset consists of user_id, product_id, preference_score(which is the number of visits made by the user for the product). The user and product ids are of type long. I have a million data points of this kind after filtering out single or double visits. I have basically written a bash script that runs the two jobs “parallelALS” and “recommendfactorized” just as shown in the example “factorize-movielens-1M”. After running the script, the resulting recommendations seem to have a bug. The format of each row of the results (as explained in several blog posts) seems to be :- user_id [product_id:score,…] However all the products_ids in every row is 0. I am not sure what is going wrong here. Is this a problem with the dataset or a matter of tuning parameters (alpha,lambda, etc) or something else? Regards, Sneha
