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From Sebastian Schelter <...@apache.org>
Subject Re: Request - Release 0.6 feature set listing
Date Tue, 21 Jun 2011 15:34:56 GMT
Hello Kumar,

Unfortunately the paper is the best documentation available for the ALS 
algorithm (together with the unit tests) and a good choice of parameters 
is to be found by experimentation.

There is also a script available that applies the factorization to the 
movielens dataset: mahout-examples/bin/factorize-movielens-1M.sh

I suggest reading the article "Matrix Factorization Techniques for 
Recommender Systems" by Yehuda Koren that offers a nice to read 
introduction to matrix factorization in CF.

   http://research.yahoo.com/pub/2859

There is no video or reference tutorial available regarding ALS or 
RecommenderJob but using the latter should be pretty straight forward. 
Feel free to ask your questions here.

--sebastian

On 20.06.2011 21:50, Kumar Kandasami wrote:
> Hello Sebastian:
>
>     I was going over the ALS-WR paper (by Yunhong Zhou, Dennis Wilkinson,
> Robert Schreiber and Rong Pan- HP Labs on Netflix dataset)  this weekend,
> and I am still trying to understand the algorithm.
>
> I am currently working on running item based recommender on the Wikipedia
> link dataset (boolean preferences) on EC2 clusters.I am interested in
> testing the ALS recommender, however, at this point I have no clear
> understanding of what user/item features mean,  and even determining the
> iterations as well as numoffeatures attribute upfront.
>
> Is there any documentation or overview on the usage of  the Mahout ALS
> implementation ?
>
> Additionally, it will save us lot of time, if you could forward any
> reference tutorial or video presentation links (similar to Item-similarity
> Job on vimeo) on RecommenderJob or ALS
>
>
> Kumar    _/|\_
> www.saisk.com
> kumar@saisk.com
> "making a profound difference with knowledge and creativity..."
>
>
> On Thu, Jun 16, 2011 at 11:48 AM, Sebastian Schelter<ssc@apache.org>  wrote:
>
>> Hello Kumar,
>>
>> Check the Mahout JIRA for features planned for 0.6 at
>> https://issues.apache.org/**jira/browse/MAHOUT<https://issues.apache.org/jira/browse/MAHOUT>
>>
>> It would be great if you could test the distributed ALS recommender that
>> uses matrix factorization. If you wanna dive into that I'm sure we'd find a
>> lot of things you could improve.
>>
>> Check it's original jira issue as a starting point:
>> https://issues.apache.org/**jira/browse/MAHOUT-542<https://issues.apache.org/jira/browse/MAHOUT-542>
>>
>> If you want something small to work, you can check
>> https://issues.apache.org/**jira/browse/MAHOUT-609<https://issues.apache.org/jira/browse/MAHOUT-609>
>>
>> Does that match what you expected? If you have any ideas yourself, feel
>> free to share them with us.
>>
>> --sebastian
>>
>>
>>
>>
>> On 16.06.2011 18:06, Kumar Kandasami wrote:
>>
>>> Hi !
>>>
>>> Could anyone point to a link that outlines the features expected in the
>>> mahout 0.6 release ? Specifically distributed recommendation engines.
>>>
>>> Also, I am currently using/working on the distributed recommendation
>>> engines
>>> on EC2 clusters - is there a way that I could contribute any code that
>>> would
>>> be in the 0.6 or future road map.
>>>
>>
>>
>>> Thank you.
>>>
>>> Kumar    _/|\_
>>> www.saisk.com
>>>
>>>
>>
>


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