David, can you please give us some details about your usecase?

It seems like you're trying to reimplement the system I described in
http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/

That system is highly optimized for a certain class of usecases and only
makes sense if you have like 100+ million datapoints and 100+
requests/second to your recommender.

If you just want to start diving into recommendation mining and build a
first system to play with, working with this article is definitely the
wrong approach. In that case, I highly suggest you get a copy of "Mahout
in Action", http://manning.com/owen/ which gives a superb introduction
to recommendation mining with mahout.

--sebastian

On 03.08.2011 14:59, David Cabanillas wrote:
> Hello Sebastian,
>
> Right now, I have the precomputed item-similarity my problem is to
> relate it with mysql.
> In section*Setting up the infrastructure for the live recommender
> system* you suggest that we should to use MySQLJDBCDataModel, tu I don't
> understand how it works.
>
> Don't have any code example to relate mahout and mysql?
> Many thanks.
>
> bye
> --david

David,

it is not helpful and frustrating, if you don't answer questions.

I think you are on a wrong track currently, to quote my blogpost: "Be
aware that this is a guide intended for readers already familiar with
Collaborative Filtering and recommender systems that are evaluating
Mahout as a choice for building their production systems on. The focus
is on making the right engineering decisions rather than on explaining
algorithms here."
And please reply to the user-mailinglist and not to me in person, the
purpose of Apache projects offering support is to have public
conversations and give all readers the possibility to learn not to have
free private consultation by the committers.
--sebastian

On 04.08.2011 11:44, David Cabanillas wrote:
> Right now I only want to connect mahout with mysql and I have not find
> any example.
> In the section *Putting the puzzle together you said:
> *
>
> DataSource datasource = ...
>
>
> But what's means ... ???
>
>
> On Thu, Aug 4, 2011 at 10:14 AM, Sebastian Schelter <[EMAIL PROTECTED]
> <mailto:[EMAIL PROTECTED]>> wrote:
>
>     David, can you please give us some details about your usecase?
>
>     It seems like you're trying to reimplement the system I described in
>     http://ssc.io/deploying-a-__massively-scalable-__recommender-system-with-__apache-mahout/
>     <http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/>
>
>     That system is highly optimized for a certain class of usecases and
>     only makes sense if you have like 100+ million datapoints and 100+
>     requests/second to your recommender.
>
>     If you just want to start diving into recommendation mining and
>     build a first system to play with, working with this article is
>     definitely the wrong approach. In that case, I highly suggest you
>     get a copy of "Mahout in Action", http://manning.com/owen/ which
>     gives a superb introduction to recommendation mining with mahout.
>
>     --sebastian
>
>
>     On 03.08.2011 14:59, David Cabanillas wrote:
>
>         Hello Sebastian,
>
>         Right now, I have the precomputed item-similarity my problem is to
>         relate it with mysql.
>         In section*Setting up the infrastructure for the live recommender
>         system* you suggest that we should to use MySQLJDBCDataModel, tu
>         I don't
>         understand how it works.
>
>         Don't have any code example to relate mahout and mysql?
>         Many thanks.
>
>         bye
>         --david
>
>
>
>
>
> --
> bye
> --david

On 04.08.2011 11:58, David Cabanillas wrote:
> Sorry Sebastien and I have only test the cf working with mysql, but I
> have not any example.
>
> Nowadays I am in an initial state, my idea is to apply similarity in a
> job portal, the idea is to recommend to unemployed skills that other
> unemployed have selected. For example, if many users knows Java and
> Pascal and a new user selects Java the system should recommend Pascal too.

How many datapoints will the system have to process for this? I'm not
sure you really need to use hadoop for that. An in-memory recommender
might be a much easier to deploy solution.

--sebastian

>
> Thanks for your support.
> PS: At the end If I would have done the solution (to use mahout with
> mysql) I have published this solution in your blog.
>
>
> On Thu, Aug 4, 2011 at 11:48 AM, Sebastian Schelter <[EMAIL PROTECTED]
> <mailto:[EMAIL PROTECTED]>> wrote:
>
>     David,
>
>     it is not helpful and frustrating, if you don't answer questions.
>
>     I think you are on a wrong track currently, to quote my blogpost:
>     "Be aware that this is a guide intended for readers already familiar
>     with Collaborative Filtering and recommender systems that are
>     evaluating Mahout as a choice for building their production systems
>     on. The focus is on making the right engineering decisions rather
>     than on explaining algorithms here."
>
>
>     And please reply to the user-mailinglist and not to me in person,
>     the purpose of Apache projects offering support is to have public
>     conversations and give all readers the possibility to learn not to
>     have free private consultation by the committers.
>
>
>     --sebastian
>
>
>     On 04.08.2011 11:44, David Cabanillas wrote:
>
>         Right now I only want to connect mahout with mysql and I have
>         not find
>         any example.
>         In the section *Putting the puzzle together you said:
>         *
>
>         DataSource datasource = ...
>
>
>         But what's means ... ???
>
>
>         On Thu, Aug 4, 2011 at 10:14 AM, Sebastian Schelter
>         <[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>
>         <mailto:[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>>> wrote:
>
>             David, can you please give us some details about your usecase?
>
>             It seems like you're trying to reimplement the system I
>         described in
>         http://ssc.io/deploying-a-____massively-scalable-____recommender-system-with-____apache-mahout/
>         <http://ssc.io/deploying-a-__massively-scalable-__recommender-system-with-__apache-mahout/>
>         <http://ssc.io/deploying-a-__massively-scalable-__recommender-system-with-__apache-mahout/
>         <http://ssc.io/deploying-a-massively-scalable-recommender-system-with-apache-mahout/>>
>
>             That system is highly optimized for a certain class of
>         usecases and
>             only makes sense if you have like 100+ million datapoints
>         and 100+
>             requests/second to your recommender.
>
>             If you just want to start diving into recommendation mining and
>             build a first system to play with, working with this article is
>             definitely the wrong approach. In that case, I highly
>         suggest you
>             get a copy of "Mahout in Action", http://manning.com/owen/ which
>             gives a superb introduction to recommendation mining with
>         mahout.
>
>             --sebastian
>
>
>             On 03.08.2011 14:59, David Cabanillas wrote:
>
>                 Hello Sebastian,
>
>                 Right now, I have the precomputed item-similarity my
>         problem is to
>                 relate it with mysql.
>                 In section*Setting up the infrastructure for the live
>         recommender
>                 system* you suggest that we should to use
>         MySQLJDBCDataModel, tu
>                 I don't
>                 understand how it works.
>
>                 Don't have any code example to relate mahout and mysql?

On 04.08.2011 12:07, David Cabanillas wrote:
> Hello,
>
> On Thu, Aug 4, 2011 at 12:04 PM, Sebastian Schelter <[EMAIL PROTECTED]
> <mailto:[EMAIL PROTECTED]>> wrote:
>
>     On 04.08.2011 11:58, David Cabanillas wrote:
>
>         Sorry Sebastien and I have only test the cf working with mysql,
>         but I
>         have not any example.
>
>         Nowadays I am in an initial state, my idea is to apply
>         similarity in a
>         job portal, the idea is to recommend to unemployed skills that other
>         unemployed have selected. For example, if many users knows Java and
>         Pascal and a new user selects Java the system should recommend
>         Pascal too.
>
>
>     How many datapoints will the system have to process for this? I'm
>     not sure you really need to use hadoop for that. An in-memory
>     recommender might be a much easier to deploy solution.
>
>
> Problably we have 1000 - 2000 users with 20-40 items per user. Do you
> know any in-memory recommender resource?

That's definitely nothing you need hadoop for. The easiest way to tackle
your usecase would be to get a copy of "Mahout in Action" and read the
recommendation chapter. This covers everything you need for your usecase.

If you don't want to spend any money, read those websites:

https://cwiki.apache.org/confluence/display/MAHOUT/Recommender+Documentation

http://blog.jteam.nl/2009/12/09/mahout-taste-part-one-introduction/

--sebastian

>
>
>     --sebastian
>
>
>         Thanks for your support.
>         PS: At the end If I would have done the solution (to use mahout with
>         mysql) I have published this solution in your blog.
>
>
>         On Thu, Aug 4, 2011 at 11:48 AM, Sebastian Schelter
>         <[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>
>         <mailto:[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>>> wrote:
>
>             David,
>
>             it is not helpful and frustrating, if you don't answer
>         questions.
>
>             I think you are on a wrong track currently, to quote my
>         blogpost:
>         "Be aware that this is a guide intended for readers already familiar
>             with Collaborative Filtering and recommender systems that are
>             evaluating Mahout as a choice for building their production
>         systems
>             on. The focus is on making the right engineering decisions
>         rather
>             than on explaining algorithms here."
>
>
>             And please reply to the user-mailinglist and not to me in
>         person,
>             the purpose of Apache projects offering support is to have
>         public
>             conversations and give all readers the possibility to learn
>         not to
>             have free private consultation by the committers.
>
>
>             --sebastian
>
>
>             On 04.08.2011 11:44, David Cabanillas wrote:
>
>                 Right now I only want to connect mahout with mysql and I
>         have
>                 not find
>                 any example.
>                 In the section *Putting the puzzle together you said:
>                 *
>
>                 DataSource datasource = ...
>
>
>                 But what's means ... ???
>
>
>                 On Thu, Aug 4, 2011 at 10:14 AM, Sebastian Schelter
>         <[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]> <mailto:[EMAIL PROTECTED]
>         <mailto:[EMAIL PROTECTED]>>
>         <mailto:[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>
>         <mailto:[EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]>>>> wrote:
>
>                     David, can you please give us some details about
>         your usecase?
>
>                     It seems like you're trying to reimplement the system I
>                 described in
>         http://ssc.io/deploying-a-______massively-scalable-______recommender-system-with-______apache-mahout/
>         <http://ssc.io/deploying-a-____massively-scalable-____recommender-system-with-____apache-mahout/>
>         <http://ssc.io/deploying-a-____massively-scalable-____recommender-system-with-____apache-mahout/