Hello, if there is a high demand for this functionality my company (http://www.apaxo.de/us/recitems.html) could implement this. Nevertheless we can't do it for free. So if it is possible to get a shared budget from everybody who is interested in this then it would be possible to write it.
The codehaus JIRA has an incentive functionality: https://secure.donay.com/site/index Perhaps this might also be useful for the Mahout (a.k.a. Apache) JIRA. /Manuel Am 20.07.2013 um 00:45 schrieb Ted Dunning: > OK. I think the crux here is the off-line to Solr part so let's see who > else pops up. > > Having a solr maven could be very helpful. > > > On Fri, Jul 19, 2013 at 3:39 PM, Luis Carlos Guerrero Covo < > [email protected]> wrote: > >> I'm currently working for a portal that has a similar use case and I was >> thinking of implementing this in a similar way. I'm generating >> recommendations using python scripts based on similarity measures (content >> based recommendation) only using euclidean distance and some weights for >> each attribute. I want to use mahout's GenericItemBasedRecommender to >> generate these same recommendations without user data (no tracking right >> now of user to item relationship). I was thinking of pushing the generated >> recommendations to solr using atomic updates since my fields are all stored >> right now. Since this is very similar to what I'm trying to accomplish, I >> would sign up to collaborate in any way I can since I'm fairly familiar >> with solr and I'm starting to learn my way around mahout. >> >> >> On Fri, Jul 19, 2013 at 5:12 PM, Sebastian Schelter <[email protected]> >> wrote: >> >>> I would also be willing to provide guidance and advice for anyone taking >>> this on, I can especially help with the offline analysis part. >>> >>> --sebastian >>> >>> >>> 2013/7/19 Ted Dunning <[email protected]> >>> >>>> I would be happy to supervise a project to implement a demo of this if >>>> anybody is willing to do the grunt work of gluing things together. >>>> >>>> Sooo, if you would like to work on this, here is a suggested project. >>>> >>>> This project would entail: >>>> >>>> a) build a synthetic data source >>>> >>>> b) write scripts to do the off-line analysis >>>> >>>> c) write scripts to export to Solr >>>> >>>> d) write a very quick web facade over Solr to make it look like a >>>> recommendation engine. This would include >>>> >>>> d.1) a "most popular page" that does combined popularity rise and >>>> recommendation >>>> >>>> d.2) a "personal recommendation page" that does just recommendation >>> with >>>> dithering >>>> >>>> d.3) item pages with "related items" at the bottom >>>> >>>> e) work with others to provide high quality system walk-through and >>> install >>>> directions >>>> >>>> If you want to bite on this, we should arrange a weekly video hangout. >> I >>>> am willing to commit to guiding and providing detailed technical >>>> approaches. You should be willing to commit to actually doing stuff. >>>> >>>> The goal would be to provide a fully worked out scaffolding of a >>> practical >>>> recommendation system that presumably would become an example module in >>>> Mahout. >>>> >>>> >>>> On Fri, Jul 19, 2013 at 1:08 PM, B Lyon <[email protected]> wrote: >>>> >>>>> +1 as well. Sounds fun. >>>>> >>>>> On Fri, Jul 19, 2013 at 4:06 PM, Dominik Hübner < >> [email protected] >>>>>> wrote: >>>>> >>>>>> +1 for getting something like that in a future release of Mahout >>>>>> >>>>>> On Jul 19, 2013, at 10:02 PM, Sebastian Schelter <[email protected]> >>>> wrote: >>>>>> >>>>>>> It would be awesome if we could get a nice, easily deployable >>>>>>> implementation of that approach into Mahout before 1.0 >>>>>>> >>>>>>> >>>>>>> 2013/7/19 Ted Dunning <[email protected]> >>>>>>> >>>>>>>> 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 < >>>>>>>> [email protected]> 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. >>>>>>>>> >>>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>>> -- >>>>> BF Lyon >>>>> http://www.nowherenearithaca.com >>>>> >>>> >>> >> >> >> >> -- >> Luis Carlos Guerrero Covo >> M.S. Computer Engineering >> (57) 3183542047 >> -- Manuel Blechschmidt M.Sc. IT Systems Engineering Dortustr. 57 14467 Potsdam Mobil: 0173/6322621 Twitter: http://twitter.com/Manuel_B
