If you only have 20 categories, I would recommend that you consider using different technologies than recommendations. Simply building 20 classifiers is likely to be as effective or more so.
I don't understand your question about the command line. On Wed, Aug 6, 2014 at 7:34 AM, xenlee - Zerg <[email protected]> wrote: > Hi, > I am building an Item Based Recommender System for 10 million users who > rate categories over 20 possible categories (new categories like politic, > sport etc...) > I would like for each one of them to be recommended at least another > category which they don't know (no rating). > > I runned a GenericUserBasedRecommender and asked for recommendations for > each user but It looks extremely long: maybe 1000 user proceeded per > minute. > My questions are: > > Can I run this same GenericUserBasedRecommender on hadoop and would it > really befaster? I saw and run an ItemBasedRecommender with command line on > a cluster, but I would prefer run a User Based one. > > Is there another smarter way to deal with my problem? Maybe some clustering > solution instead of recommendation? I don't exactly see how. > > Finally, am I right when I say that the algorithms who have no command line > are not to use with hadoop? > > > Thank you for your answers, > > xenlee - >
