Thank you for your reply, i will think about it !
For my question with the command line, It is just that I don't really
understand which algorithms can be used on a hadoop cluster, and which
can not. And for those which can, how can I call them if not using the
command line like "mahout recommendItemBased --input ... -output ... -s
PearsonCorrelationSimilarity".
Le 06/08/2014 20:16, Ted Dunning a écrit :
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 <sc2.xen...@gmail.com> 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 -
