mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Dmitriy Lyubimov <dlie...@gmail.com>
Subject Re: Setting up a recommender
Date Fri, 19 Jul 2013 22:05:41 GMT
On Fri, Jul 19, 2013 at 12:59 PM, Ted Dunning <ted.dunning@gmail.com> wrote:

> My current advice is to use Hadoop (if necessary) to build a sparse
> item-item matrix based on each kind of behavior you have and then drop
> those similarities into a search engine

you mean like Lucene / Katta?


> to deliver the actual
> recommendations.  This allows lots of flexibility in terms of which kinds
> of inputs you use for the recommendation and lets you blend recommendations
> with search and geo-location.
>
>
> On Fri, Jul 19, 2013 at 12:33 PM, Helder Martins <
> helder.garay@corp.terra.com.br> wrote:
>
> > Hi,
> > I'm a dev working for a web portal in Brazil and I'm particularly
> > interested in building a item-based collaborative filtering recommender
> > for our database of news articles.
> > After some coding, I was able to get some recommendations using a
> > GenericItemBasedRecommender, a CassandraDataModel and some custom
> > classes that store item similarities and migrated item IDs into
> > Cassandra. But know I'm in doubt of what is normally done with this
> > recommender: Should I run this as a daemon, cache the recommendations
> > into memory and set up a web service to consult it online? Should I pre
> > process these recommendations for each recent user and store it
> > somewhere? My first idea was storing all these recs back into Cassandra,
> > but looking into some classes it seems to me that the norm is to read
> > the input data and store the output always using files. Is this a common
> > practice that benefits from HDFS?
> > My use case here is something around 70k recommendations requests per
> > second.
> >
> > Thanks in advance,
> >
> > --
> >
> > Atenciosamente
> > Helder Martins
> > Arquitetura do Portal e Sistemas de Backend
> > +55 (51) 3284-4475
> > Terra
> >
> >
> > Esta mensagem e seus anexos se dirigem exclusivamente ao seu
> destinatário,
> > podem conter informação privilegiada ou confidencial e são de uso
> exclusivo
> > da pessoa ou entidade de destino. Se não for destinatário desta mensagem,
> > fica notificado de que a leitura, utilização, divulgação e/ou cópia sem
> > autorização pode estar proibida em virtude da legislação vigente. Se
> > recebeu esta mensagem por engano, pedimos que nos o comunique
> imediatamente
> > por esta mesma via e, em seguida, apague-a.
> >
> > Este mensaje y sus adjuntos se dirigen exclusivamente a su destinatario,
> > puede contener información privilegiada o confidencial y es para uso
> > exclusivo de la persona o entidad de destino. Si no es usted él
> > destinatario indicado, queda notificado de que la lectura, utilización,
> > divulgación y/o copia sin autorización puede estar prohibida en virtud de
> > la legislación vigente. Si ha recibido este mensaje por error, le pedimos
> > que nos lo comunique inmediatamente por esta misma vía y proceda a su
> > exclusión.
> >
> > The information contained in this transmissión is privileged and
> > confidential information intended only for the use of the individual or
> > entity named above. If the reader of this message is not the intended
> > recipient, you are hereby notified that any dissemination, distribution
> or
> > copying of this communication is strictly prohibited. If you have
> received
> > this transmission in error, do not read it. Please immediately reply to
> the
> > sender that you have received this communication in error and then delete
> > it.
> >
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message