Implicit feedback recommendation - Incorrect results

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