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 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.ga...@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. >
