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 <f.ozgur.catak@gmail.com> 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 <srowen@gmail.com> 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 <f.ozgur.catak@gmail.com>
>> 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
>> >
>>
>
