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 >> > >> >
