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Posted to user@mahout.apache.org by "F.Ozgur Catak" <f....@gmail.com> on 2009/12/10 20:16:30 UTC
ItemBasedRecommender
Hi,
I think there's a lack of help file for this project. I started to use
recommendation system. I use GenericItemBasedRecommender. How can I find
documentation for this class.
Thanks
Ozgur CATAK
Re: ItemBasedRecommender
Posted by Sean Owen <sr...@gmail.com>.
On Fri, Dec 11, 2009 at 2:47 PM, F.Ozgur Catak <f....@gmail.com> wrote:
> and Converted using MemoryIDMigrator to long values like
>
> 1903325046098094985,5192157078505275458,-3162216497309240828
I don't understand what the third value is? I think this is your
problem -- should be a preference value.
> When i used Euclidean distance there is no recommendation, but Log
> likelihood Based Item Similarity gives me results which seems very good.
> So, If I use string based input data for recommendation, do I have to use
> "Log likelihood Based Item Similarity"?
You would have to tell me more about your data (see question above)
and what code you are using. Something tells me you have accidentally
transformed your *preference values* or something and are using
"-3162216497309240828" as a preference value! This would mean all
Euclidean distance similarities round to 0, so nothing can be
recommended.
Log-likelihood ignores preference value so isn't affected by the issue above.
Re: ItemBasedRecommender
Posted by "F.Ozgur Catak" <f....@gmail.com>.
I used Log Likelihood Similarity and Euclidean distance. My input file is
string
CustomerNo,Part No
TR433;SPTBY-1711
TR433;SPTBL-1711
TR433;SPTKP-1711
TR746;TDTBY-861
TR746;TDTBL-861
TR746;TDTKP-861
and Converted using MemoryIDMigrator to long values like
1903325046098094985,5192157078505275458,-3162216497309240828
2276278324672472631,496035984324855953,-3162216497309240828
2276278324672472631,2666580089560192147,-3162216497309240828
2276278324672472631,-3436879215117796241,-3162216497309240828
7260913912542566719,8688228931167592947,-3162216497309240828
7260913912542566719,5860894063367472580,-3162216497309240828
When i used Euclidean distance there is no recommendation, but Log
likelihood Based Item Similarity gives me results which seems very good.
So, If I use string based input data for recommendation, do I have to use
"Log likelihood Based Item Similarity"?
Thanks
Ozgur CATAK
Ph.D. Student
Istanbul University, Informatics
On Fri, Dec 11, 2009 at 12:13 PM, Sean Owen <sr...@gmail.com> wrote:
> You probably want a user-based recommender since you have very few
> users, relatively. Performance should not be a problem given the size
> of your input -- probably can compute recommendations in tens of
> milliseconds.
>
> You will need to use RecommenderEvaluator to find which of many
> possible implementations produces the best results on your input. For
> example, experiment with a nearest-n user neighborhood with small
> values of n, and try Euclidean distance-based and log-likelihood-based
> similarity metrics. Try several variations and see which produces the
> lowest evaluation score.
>
> On Fri, Dec 11, 2009 at 6:43 AM, F.Ozgur Catak <f....@gmail.com>
> wrote:
> > approx. 100.000 rows and 2000 users
> >
> > On Fri, Dec 11, 2009 at 2:25 AM, Sean Owen <sr...@gmail.com> 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 <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 <sr...@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
> >> >> >
> >> >>
> >> >
> >>
> >
>
Re: ItemBasedRecommender
Posted by Sean Owen <sr...@gmail.com>.
You probably want a user-based recommender since you have very few
users, relatively. Performance should not be a problem given the size
of your input -- probably can compute recommendations in tens of
milliseconds.
You will need to use RecommenderEvaluator to find which of many
possible implementations produces the best results on your input. For
example, experiment with a nearest-n user neighborhood with small
values of n, and try Euclidean distance-based and log-likelihood-based
similarity metrics. Try several variations and see which produces the
lowest evaluation score.
On Fri, Dec 11, 2009 at 6:43 AM, F.Ozgur Catak <f....@gmail.com> wrote:
> approx. 100.000 rows and 2000 users
>
> On Fri, Dec 11, 2009 at 2:25 AM, Sean Owen <sr...@gmail.com> 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 <f....@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 <sr...@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
>> >> >
>> >>
>> >
>>
>
Re: ItemBasedRecommender
Posted by "F.Ozgur Catak" <f....@gmail.com>.
approx. 100.000 rows and 2000 users
On Fri, Dec 11, 2009 at 2:25 AM, Sean Owen <sr...@gmail.com> 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 <f....@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 <sr...@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
> >> >
> >>
> >
>
Re: ItemBasedRecommender
Posted by Sean Owen <sr...@gmail.com>.
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....@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 <sr...@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....@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
>> >
>>
>
Re: ItemBasedRecommender
Posted by "F.Ozgur Catak" <f....@gmail.com>.
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 <sr...@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....@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
> >
>
Re: ItemBasedRecommender
Posted by Sean Owen <sr...@gmail.com>.
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....@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
>
Re: ItemBasedRecommender
Posted by "F.Ozgur Catak" <f....@gmail.com>.
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
On Thu, Dec 10, 2009 at 9:21 PM, Sean Owen <sr...@gmail.com> wrote:
> The documentation is really in the javadoc. Have you checked that out?
> It really works in much the same way as the other Recommenders -- same
> interface.
>
> Is there some aspect you would like more information about? I can add
> documentation on those parts.
>
> On Thu, Dec 10, 2009 at 7:16 PM, F.Ozgur Catak <f....@gmail.com>
> wrote:
> > Hi,
> >
> > I think there's a lack of help file for this project. I started to use
> > recommendation system. I use GenericItemBasedRecommender. How can I find
> > documentation for this class.
> >
> > Thanks
> >
> > Ozgur CATAK
> >
>
Re: ItemBasedRecommender
Posted by Sean Owen <sr...@gmail.com>.
The documentation is really in the javadoc. Have you checked that out?
It really works in much the same way as the other Recommenders -- same
interface.
Is there some aspect you would like more information about? I can add
documentation on those parts.
On Thu, Dec 10, 2009 at 7:16 PM, F.Ozgur Catak <f....@gmail.com> wrote:
> Hi,
>
> I think there's a lack of help file for this project. I started to use
> recommendation system. I use GenericItemBasedRecommender. How can I find
> documentation for this class.
>
> Thanks
>
> Ozgur CATAK
>