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From Manuel Blechschmidt <>
Subject Re: Mahout performance issues
Date Thu, 01 Dec 2011 15:18:11 GMT
Hi Dan,

On 30.11.2011, at 21:23, Dan Beaulieu wrote:

> Hi all, this is a tangent and can mostly be ignored by the people
> interested in this problem.
> I'm new to Machine Learning and especially Mahout. Following this
> discussion has made me a bit confused.
> Isn't Mahout used for large datasets where it makes sense to distribute the
> work? Why then isn't anyone pointing
> out that the problem may be the use of one single Mahout node? Is it
> because it's boolean based? Is it because the data set
> isn't really that large?

Isabel already gave a good explanation. Nevertheless as it turns out at the moment the problem
of this performance issues seams to be the item similarity.

There is a distributed approach of calculating this data:

Sebastian Schelter wrote a tutorial how to use this job:

Nevertheless not everybody is maintaining a hadoop cluster. For example I did not use a cluster
yet. As a rule of thumb (by Sean Owen) you can calculate everything until 100.000.000 Ratings
on your normal machine.

> Even if for whatever reason a single node will do for this case, is it
> really expected that the recommendation process would finish in less than
> half a second?

Yes, it is. Recommendation is a real time problem but how to do it in realtime is still a
question where a lot of research is put in. A lot of people from mahout are working in an
academic context so it is unclear yet how to handle the different problems.
Mahout has a lot of possibilities to tweak. For a small dataset I did a benchmark published

Actually for every recommender there is a trade off between:
- accuracy
- space
- time

It is a tough task to find the sweet spot.

> This makes me think if that is the expectation then the data set is
> actually small and Mahout might be overkill...
> What obvious piece of the Mahout puzzle am I missing?

Hope that helps

> Thanks.
> Dan
> On Wed, Nov 30, 2011 at 11:56 AM, Sean Owen <> wrote:
>> Have you used CachingItemSimilarity? That will hold common similarities in
>> memory. It's a lot easier than pre-computing and might help.
>> I think something like your change is a good one (Sebastian what do you
>> think) in that it gives you the ultimate lever to control how many
>> candidates are evaluated. That ought to make it go as fast as you like, but
>> it trades off quality. Still I'd be really surprised if there's no viable
>> middle ground -- this works fine at smaller scale, where 100s of candidates
>> are evaluated, perhaps, and you can use your lever to get to 100s of
>> candidates at your scale too. Is that still both slow and inaccurate?
>> On Wed, Nov 30, 2011 at 3:18 PM, Daniel Zohar <> wrote:
>>> I just tested the app with Mahout 0.6.
>>> There seems to be a small performance improvement, but still
>>> recommendations for the 'heavy users' take between 1-5 seconds.

Manuel Blechschmidt
Dortustr. 57
14467 Potsdam
Mobil: 0173/6322621

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