approx. 100.000 rows and 2000 users On Fri, Dec 11, 2009 at 2:25 AM, Sean Owen <[email protected]> wrote:
> The best algorithm really depends on your data. > > How many items and how many users do you have? that will determine > which algorithms will perform better. > > Which algorithms will produce the best recommendations is hard to > tell. Usually you have to use RecommenderEvaluator with lots of > implementations and your data to find which seems to work best. > > if you can say more about your data, maybe I can guess about the best > implementations to try. > > On Thu, Dec 10, 2009 at 9:56 PM, F.Ozgur Catak <[email protected]> > wrote: > > Hi again, > > > > Finally I understand the item similarity :). In our b2b project we need > to > > develop a recommendation system. I want to use mahout. Is there any best > > practice. And also another question, is mahout enogh mature to use our > > production enviroment. > > > > thanks > > > > On Thu, Dec 10, 2009 at 9:31 PM, Sean Owen <[email protected]> wrote: > > > >> No, the similarity metric is passed in as an ItemSimilarity metric. > >> There is no implementation based on a model, if that's what you mean. > >> What else? > >> > >> On Thu, Dec 10, 2009 at 7:27 PM, F.Ozgur Catak <[email protected] > > > >> wrote: > >> > Yes, I read the javadoc but i need the algorithms. For example, does > >> > recommandation system uses apriori algorithm to find similar values? > etc. > >> > > >> > Maybe it is mine problem, because I'm also a newbi about data mining. > >> > > >> > Thanks > >> > > >> > > >
