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From Pat Ferrel <>
Subject Re: fast performance way of writing preferences to file?
Date Mon, 06 Apr 2015 21:00:53 GMT
Sorry, we are trying to get a release out.

You can look at a custom similarity measure. Look at where SIMILARITY_COSINE leads you and
customize that maybe? There are in-memory and mapreduce versions and not sure which you are
using. That is code I haven’t looked at for a long time so can’t get you much closer.

On Apr 3, 2015, at 10:52 AM, PierLorenzo Bianchini <> wrote:

Hi again,
seeing the answers to this question and the other I had posted ("adjusted cosine similarity
for item-based recommender?"), I think I should clarify a bit what I'm trying to achieve and
why I (believe I should) do things the way I'm doing.

I'm doing a class called "Learning from User-Generated data". Our first assignment deals with
analysing the results of various types of recommenders. I'll go as far as saying "old-school"
recommenders, given the content of your answers.
We have been introduced to:
* Memory based:
    - user-based
    - item-based (*with* adjusted cosine similarity!)
    - slope-one
    - graph-based transitivity
* Memory based
    - preprocessed item/user based (? this is unclear to me but I didn't reach this part of
the assignment so I'll search for information before I ask questions; I also found an article
where they mentioned slope-one amongst the model based; I guess I'll need to do more research
on this)
    - matrix factorization-based (I saw that SVD is available in Mahout; my project partner
is looking into that right now)

We have a *static* training dataset (800.000 <user,movie,preference> triples) and another
static dataset for which we have to extract the predicted preferences (200.000 <user,movie>
tuples) and write them back to a movie (i.e. recompose the <user,movie,preference> triples).
Note that this will never go in a production environment, as it is merely a university requirement.
For the same reason, I would prefer not to mix up things too much and I'd rather do a step-by-step
learning (i.e. focus on Mahout for now, before I dig deeper and check the search-based approach,
which uses DB-mahout-solr-spark... maybe a bit too much to handle at once with the deadline
we were given).

So if I might get back to my original questions (again, I'm sorry for being stubborn but I'm
under specific constraints - I'll really try to understand the search-based approach when
I have more time) ;)
1. I'm guessing that to implement an adjusted cosine similarity I should extend AbstractSimilarity
(or maybe even AbstractRecommender?). Is this right?
2. I still can't believe that it takes more than at-most a few minutes to go through my 200.000
lines and find the already calculated preference. What am I doing wrong? :/ Should I store
my whole datamodel in a file (how?) and then read through the file? I don't see how this could
be faster than just reading the exact value I'm searching for...

Thanks again for your answers! Regards,

Pier Lorenzo

On Fri, 4/3/15, Ted Dunning <> wrote:

Subject: Re: fast performance way of writing preferences to file?
To: "" <>
Date: Friday, April 3, 2015, 5:52 PM

Are you sure that the
problem is writing the results?  It seems to me that
the real problem is the use of a user-based

For such a
small data set, for instance, a search-based recommender
will be
able to make recommendations in less
than a millisecond with multiple
recommendations possible in parallel.  This
should allow you to do 200,000
recommendations in a few minutes on a single

With such a small
dataset, indicator-based methods may not be the best
option.  To improve that, try using something
larger such as the million
song dataset. 

Also, using and estimating
ratings is not a particularly good thing to be
doing if you want to build a real

Fri, Apr 3, 2015 at 3:26 AM, PierLorenzo Bianchini <>

> Hello
> I'm new to mahout, to
recommender systems and to the mailing list.
> I''m trying
to find a (fast) way to write back preferences to a file.
> tried a few methods but I'm sure
there must be a better approach.
Here's the deal (you can find the same post in
> I have a training
dataset of 800.000 records from 6000 users rating 3900
> movies. These are stored in a comma
separated file like:
userId,movieId,preference. I have another dataset (200.000
records) in the
> format: userId,movieId.
My goal is to use the first dataset as a
> training-set, in order to determine the
missing preferences of the second
> So far, I
managed to load the training dataset and I generated
> recommendations. This is
pretty smooth and doesn't take too much time. But
> I'm struggling when it comes to
writing back the recommendations.
> The first method I tried is:
>   * read a line from
the file and get the userId,movieId tuple.
>   * retrieve the calculated preference
with estimatePreference(userId,
>   * append the preference to
the line and save it in a new file
> This
works, but it's incredibly slow (I added a counter to
print every
> 10.000th iteration: after a
couple of minutes it had only printed once. I
> have 8GB-RAM with an i7-core... how long
can it take to process 200.000
> My second
choise was:
>   *
create a new FileDataModel with the second dataset
>   * do something like this:
newDataModel.setPreference(userId, movieId,
> recommender.estimatePreference(userId,
> Here I
get several problems:
>   * at runtime:
java.lang.UnsupportedOperationException (as I found out
> [2], FileDataModel actually
can't be updated. I don't understand why the
> function setPreference exists in the first
>   * The API of
FileDataModel#setPreference states "This method should
> be considered relatively
> I read
around that a solution would be to use delta files, but I
> find out what that
actually means. Any suggestion on how I could speed up
> my writing-the-preferences process?
> Thank you!
> Pier Lorenzo
> [1]
> [2]

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