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Posted to user@mahout.apache.org by Wei Li <we...@gmail.com> on 2014/09/15 09:15:35 UTC
Question on RecommenderJob
Hi Mahout Users:
We are using the RecommderJob to perform the item-based
recommendations, the following settings are used:
similairtyClassname=SIMILARITY_COOCCURRENCE
numRecommendations=20
other parameters are set to default values
while we see that the size of the recommendation results for some users is
less than 20, only 1 or 2. Since we have no time to dive into the source
code now, we do know if we see the right parameters. Does any one can help
us on this issue? many thanks :)
Best
Wei
Re: Question on RecommenderJob
Posted by Wei Li <we...@gmail.com>.
OK, got your points, thanks Ferrel and Peng.
On Sun, Sep 21, 2014 at 11:39 PM, Pat Ferrel <pa...@gmail.com> wrote:
> @li
>
> Mahout down-samples the input data based on how “important” the
> cooccurrence of interactions seems to be. I’d use SIMILARITY_LOGLIKELIHOOD
> for the best measure of this. You will still get no recs for some users if
> they don’t have enough interactions or they are not “important”
> interactions.
>
> On Sep 15, 2014, at 7:38 PM, Peng Zhang <pz...@gmail.com> wrote:
>
> As far as I know, mahout would not do any post processing to remove
> records from recs. I assume you refer to recs as recommendation candidates.
> If you find some user is not in recs (the output), please make sure he/she
> has sufficient interactions with the items.
>
> On Sep 16, 2014, at 10:17 AM, Wei Li <we...@gmail.com> wrote:
>
> > Thanks Peng.
> >
> > Yes, I agree your points, there may not be enough interactions between
> > users and items to do the recommendations, but do our Mahout code does
> some
> > extra filtering to remove the recommendation candidates?
> >
> > On Mon, Sep 15, 2014 at 3:35 PM, Peng Zhang <pz...@gmail.com>
> wrote:
> >
> >> Mahout does not guarantee specified recs for each user. There are many
> >> reasons, e.g, there might not be enough similar users or items for a
> user.
> >>
> >> Peng Zhang
> >>
> >> --
> >> Sent from my iPhone
> >>
> >>> On Sep 15, 2014, at 3:15 PM, Wei Li <we...@gmail.com> wrote:
> >>>
> >>> Hi Mahout Users:
> >>>
> >>> We are using the RecommderJob to perform the item-based
> >>> recommendations, the following settings are used:
> >>>
> >>> similairtyClassname=SIMILARITY_COOCCURRENCE
> >>> numRecommendations=20
> >>> other parameters are set to default values
> >>>
> >>> while we see that the size of the recommendation results for some users
> >> is
> >>> less than 20, only 1 or 2. Since we have no time to dive into the
> source
> >>> code now, we do know if we see the right parameters. Does any one can
> >> help
> >>> us on this issue? many thanks :)
> >>>
> >>> Best
> >>> Wei
> >>
>
>
>
Re: Question on RecommenderJob
Posted by Pat Ferrel <pa...@gmail.com>.
@li
Mahout down-samples the input data based on how “important” the cooccurrence of interactions seems to be. I’d use SIMILARITY_LOGLIKELIHOOD for the best measure of this. You will still get no recs for some users if they don’t have enough interactions or they are not “important” interactions.
On Sep 15, 2014, at 7:38 PM, Peng Zhang <pz...@gmail.com> wrote:
As far as I know, mahout would not do any post processing to remove records from recs. I assume you refer to recs as recommendation candidates.
If you find some user is not in recs (the output), please make sure he/she has sufficient interactions with the items.
On Sep 16, 2014, at 10:17 AM, Wei Li <we...@gmail.com> wrote:
> Thanks Peng.
>
> Yes, I agree your points, there may not be enough interactions between
> users and items to do the recommendations, but do our Mahout code does some
> extra filtering to remove the recommendation candidates?
>
> On Mon, Sep 15, 2014 at 3:35 PM, Peng Zhang <pz...@gmail.com> wrote:
>
>> Mahout does not guarantee specified recs for each user. There are many
>> reasons, e.g, there might not be enough similar users or items for a user.
>>
>> Peng Zhang
>>
>> --
>> Sent from my iPhone
>>
>>> On Sep 15, 2014, at 3:15 PM, Wei Li <we...@gmail.com> wrote:
>>>
>>> Hi Mahout Users:
>>>
>>> We are using the RecommderJob to perform the item-based
>>> recommendations, the following settings are used:
>>>
>>> similairtyClassname=SIMILARITY_COOCCURRENCE
>>> numRecommendations=20
>>> other parameters are set to default values
>>>
>>> while we see that the size of the recommendation results for some users
>> is
>>> less than 20, only 1 or 2. Since we have no time to dive into the source
>>> code now, we do know if we see the right parameters. Does any one can
>> help
>>> us on this issue? many thanks :)
>>>
>>> Best
>>> Wei
>>
Re: Question on RecommenderJob
Posted by Peng Zhang <pz...@gmail.com>.
As far as I know, mahout would not do any post processing to remove records from recs. I assume you refer to recs as recommendation candidates.
If you find some user is not in recs (the output), please make sure he/she has sufficient interactions with the items.
On Sep 16, 2014, at 10:17 AM, Wei Li <we...@gmail.com> wrote:
> Thanks Peng.
>
> Yes, I agree your points, there may not be enough interactions between
> users and items to do the recommendations, but do our Mahout code does some
> extra filtering to remove the recommendation candidates?
>
> On Mon, Sep 15, 2014 at 3:35 PM, Peng Zhang <pz...@gmail.com> wrote:
>
>> Mahout does not guarantee specified recs for each user. There are many
>> reasons, e.g, there might not be enough similar users or items for a user.
>>
>> Peng Zhang
>>
>> --
>> Sent from my iPhone
>>
>>> On Sep 15, 2014, at 3:15 PM, Wei Li <we...@gmail.com> wrote:
>>>
>>> Hi Mahout Users:
>>>
>>> We are using the RecommderJob to perform the item-based
>>> recommendations, the following settings are used:
>>>
>>> similairtyClassname=SIMILARITY_COOCCURRENCE
>>> numRecommendations=20
>>> other parameters are set to default values
>>>
>>> while we see that the size of the recommendation results for some users
>> is
>>> less than 20, only 1 or 2. Since we have no time to dive into the source
>>> code now, we do know if we see the right parameters. Does any one can
>> help
>>> us on this issue? many thanks :)
>>>
>>> Best
>>> Wei
>>
Re: Question on RecommenderJob
Posted by Wei Li <we...@gmail.com>.
Thanks Peng.
Yes, I agree your points, there may not be enough interactions between
users and items to do the recommendations, but do our Mahout code does some
extra filtering to remove the recommendation candidates?
On Mon, Sep 15, 2014 at 3:35 PM, Peng Zhang <pz...@gmail.com> wrote:
> Mahout does not guarantee specified recs for each user. There are many
> reasons, e.g, there might not be enough similar users or items for a user.
>
> Peng Zhang
>
> --
> Sent from my iPhone
>
> > On Sep 15, 2014, at 3:15 PM, Wei Li <we...@gmail.com> wrote:
> >
> > Hi Mahout Users:
> >
> > We are using the RecommderJob to perform the item-based
> > recommendations, the following settings are used:
> >
> > similairtyClassname=SIMILARITY_COOCCURRENCE
> > numRecommendations=20
> > other parameters are set to default values
> >
> > while we see that the size of the recommendation results for some users
> is
> > less than 20, only 1 or 2. Since we have no time to dive into the source
> > code now, we do know if we see the right parameters. Does any one can
> help
> > us on this issue? many thanks :)
> >
> > Best
> > Wei
>
Re: Question on RecommenderJob
Posted by Peng Zhang <pz...@gmail.com>.
Mahout does not guarantee specified recs for each user. There are many reasons, e.g, there might not be enough similar users or items for a user.
Peng Zhang
--
Sent from my iPhone
> On Sep 15, 2014, at 3:15 PM, Wei Li <we...@gmail.com> wrote:
>
> Hi Mahout Users:
>
> We are using the RecommderJob to perform the item-based
> recommendations, the following settings are used:
>
> similairtyClassname=SIMILARITY_COOCCURRENCE
> numRecommendations=20
> other parameters are set to default values
>
> while we see that the size of the recommendation results for some users is
> less than 20, only 1 or 2. Since we have no time to dive into the source
> code now, we do know if we see the right parameters. Does any one can help
> us on this issue? many thanks :)
>
> Best
> Wei