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From Sofia Georgiakaki <geosofie_...@yahoo.com>
Subject Re: product recommendations engine
Date Tue, 19 Feb 2013 09:09:11 GMT
Good morning,

Myrrix provides a Recommender that implements a specific recommendation algorithm based on
matrix factorization, which is generally efficient in most cases. However, depending on your
data and access pattern, it may be better to use Mahout as well, as it provides many different
Recommenders. So you can evaluate each implementation and use the recommender that the given
time best suits your dataset.

Regards,
Sofia





>________________________________
> From: Manoj Babu <manoj444@gmail.com>
>To: user@hadoop.apache.org 
>Sent: Tuesday, February 19, 2013 7:03 AM
>Subject: Re: product recommendations engine
> 
>
>Hi Sofia,
>
>I am just hearing about the Myrrix project looks interesting. Thanks for sharing the
information.
>
>
>Cheers!
>Manoj.
>
>
>On Tue, Feb 19, 2013 at 12:45 AM, Douglass Davis <douglassdavis50@gmail.com> wrote:
>
>Ok thanks.  Myrrix looks like it has much of the set-up work done so I am taking a closer
look at that.
>>
>>
>>
>>
>>On Mon, Feb 18, 2013 at 4:00 AM, Sofia Georgiakaki <geosofie_tuc@yahoo.com>
wrote:
>>
>>Hello Douglass,
>>>
>>>you could take a look at Mahout and Myrrix projects. These are two projects thatprovide
implementations of recommendation & machine learning algorithms. There are MapReduce implementations
as well, to support massive datasets.
>>>In addition, these systems provide client APIs/various integration points, so
its easy to integrate them to your system.
>>>
>>>Regards,
>>>Sofia
>>>
>>>
>>>
>>>
>>>
>>>
>>>>________________________________
>>>> From: Douglass Davis <douglassdavis50@gmail.com>
>>>>To: user@hadoop.apache.org 
>>>>Sent: Monday, February 18, 2013 1:21 AM
>>>>Subject: product recommendations engine
>>>> 
>>>>
>>>>
>>>>Hello,
>>>>
>>>>I don't have any prior experience with Hadoop.  I am also not a statistics
expert.  I am a software engineer, however, after looking at the docs, Hadoop still seems
pretty intimidating to set up.  
>>>>
>>>>I am interested in doing product recommendations.  However, I want to store
many things about user behavior, for example whether they click on a link in an email, how
they rate a product, whether they buy it, etc.  Then I would like to come up with similar
items that a user may like.  I have seen an example just based on user ratings, but would
like to add much more data.
>>>>
>>>>Also, I think the clustering could be used in terms of recommending based
on similar descriptions, attributes, and keywords. 
>>>>
>>>>Or, I could use a combination of the two approaches.
>>>>
>>>>Another question, I wonder if Hadoop takes into account the passage of time. 
For example, a user may rate something high, then change their rating a couple months later.
>>>>
>>>>Lastly, my site is based on PHP.  I need to be able to integrate that with
Hadoop.
>>>>
>>>>How feasible is this approach?  I saw a clustering example, and a recommendation
example based on user ratings.  Are there any other advice, docs, or examples that you could
point me to that deals with any of these issues?
>>>>
>>>>Thanks,
>>>>Doug
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>
>
>
>
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