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From Pat Ferrel <...@occamsmachete.com>
Subject Re: Implicit feedback recommendation - Incorrect results
Date Tue, 08 Jul 2014 17:35:25 GMT
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 <snehayv@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


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