You are viewing a plain text version of this content. The canonical link for it is here.
Posted to user@mahout.apache.org by Deepak Subhramanian <de...@gmail.com> on 2013/09/28 14:24:19 UTC
Getting rating for all the files
Hi
I am trying to predict the ratings for some items for some users using item
based collaborative filtering. I tried using the mahout recommenditembased
, but it shows only the top 10 items or I can increase it by passing the
--numRecommendations parameter. But it doesnt shows items which has lower
predicted rating . What is the best approach to get ratings for items which
has low predicted rating ?
I tried this command.
mahout recommenditembased --input mahoutrecoinput --usersFile
recouserlist --itemsFile recoitemlist --output
/mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
--numRecommendations 4000 --maxPrefsPerUser 4000
Also I tried using the estimatePreference method on the recommender.
Please help .
Re: Getting rating for all the files
Posted by Sebastian Schelter <ss...@googlemail.com>.
There is no hadoop-based userbased recommender at the moment.
On 01.10.2013 01:13, Martin, Nick wrote:
> Hi all,
>
> I have the same question as Deepak does below...where can I find the User based recommender via Mahout command line?
>
> I don't see it listed in the valid program names:
>
> Valid program names are:
> arff.vector: : Generate Vectors from an ARFF file or directory
> baumwelch: : Baum-Welch algorithm for unsupervised HMM training
> canopy: : Canopy clustering
> cat: : Print a file or resource as the logistic regression models would see it
> cleansvd: : Cleanup and verification of SVD output
> clusterdump: : Dump cluster output to text
> clusterpp: : Groups Clustering Output In Clusters
> cmdump: : Dump confusion matrix in HTML or text formats
> cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
> cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
> dirichlet: : Dirichlet Clustering
> eigencuts: : Eigencuts spectral clustering
> evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes
> fkmeans: : Fuzzy K-means clustering
> fpg: : Frequent Pattern Growth
> hmmpredict: : Generate random sequence of observations by given HMM
> itemsimilarity: : Compute the item-item-similarities for item-based collaborative filtering
> kmeans: : K-means clustering
> lucene.vector: : Generate Vectors from a Lucene index
> matrixdump: : Dump matrix in CSV format
> matrixmult: : Take the product of two matrices
> meanshift: : Mean Shift clustering
> minhash: : Run Minhash clustering
> parallelALS: : ALS-WR factorization of a rating matrix
> recommendfactorized: : Compute recommendations using the factorization of a rating matrix
> recommenditembased: : Compute recommendations using item-based collaborative filtering
> regexconverter: : Convert text files on a per line basis based on regular expressions
> rowid: : Map SequenceFile<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>}
> rowsimilarity: : Compute the pairwise similarities of the rows of a matrix
> runAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression model
> runlogistic: : Run a logistic regression model against CSV data
> seq2encoded: : Encoded Sparse Vector generation from Text sequence files
> seq2sparse: : Sparse Vector generation from Text sequence files
> seqdirectory: : Generate sequence files (of Text) from a directory
> seqdumper: : Generic Sequence File dumper
> seqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archives
> seqwiki: : Wikipedia xml dump to sequence file
> spectralkmeans: : Spectral k-means clustering
> split: : Split Input data into test and train sets
> splitDataset: : split a rating dataset into training and probe parts
> ssvd: : Stochastic SVD
> svd: : Lanczos Singular Value Decomposition
> testnb: : Test the Vector-based Bayes classifier
> trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model
> trainlogistic: : Train a logistic regression using stochastic gradient descent
> trainnb: : Train the Vector-based Bayes classifier
> transpose: : Take the transpose of a matrix
> validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data set
> vecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectors
> vectordump: : Dump vectors from a sequence file to text
> viterbi: : Viterbi decoding of hidden states from given output states sequence
>
> -----Original Message-----
> From: Deepak Subhramanian [mailto:deepak.subhramanian@gmail.com]
> Sent: Sunday, September 29, 2013 4:06 PM
> To: user@mahout.apache.org
> Subject: Re: Getting rating for all the files
>
> I tried writing a UserRecommendation program in java. But it give me less results than the ItemBasedRecommendation. Anyone else have any thoughts on my previous question ?
>
>
> On Sun, Sep 29, 2013 at 7:24 PM, Deepak Subhramanian < deepak.subhramanian@gmail.com> wrote:
>
>> Thanks Nick. I am planning to give a try with userbasedrecommendation
>> since there are low no of users. I dont see recommenduserbased option
>> in the commandline utility for Mahout. Does that mean I have to write
>> a Java Program to use the UserBasedRecommender ?
>>
>>
>> On Sun, Sep 29, 2013 at 7:22 PM, Martin, Nick <Ni...@pssd.com> wrote:
>>
>>> I'l need to defer to one of the other math whizzes on the potential
>>> reasons for recommendations for certain users not appearing. My
>>> suspicion is that you would either not have sufficient co-occurrence
>>> of specific users/items to support a recommendation or you may need
>>> to experiment with a different similarity measure.
>>>
>>> Anyone else want to weigh in?
>>>
>>>
>>>
>>> Sent from my iPhone
>>>
>>> On Sep 29, 2013, at 1:14 PM, "Deepak Subhramanian" <
>>> deepak.subhramanian@gmail.com> wrote:
>>>
>>>> Sorry . My mistake . I am getting the lower ratings for some of the
>>> users
>>>> and items. But my issue is not solved . I am not getting ratings
>>>> for
>>> some
>>>> of the users and some of the ratings.
>>>>
>>>> My userFile has 8000 users and my itemsFile has 4000 Items . But I
>>>> get recommendations for only 5000 users and 1500 items. And the
>>>> maximum no
>>> of
>>>> recommendations given is 258. What can be the reasons that there
>>>> is no items recommendations for 3000 users and 2500 items. Is it
>>>> because
>>> there is
>>>> no similarities exist between those users and items ?
>>>>
>>>>
>>>> On Sun, Sep 29, 2013 at 4:46 PM, Deepak Subhramanian <
>>>> deepak.subhramanian@gmail.com> wrote:
>>>>
>>>>> Thanks Nick. As I mentioned earleir I am getting ratings only for
>>>>> the
>>> top
>>>>> recommended products instead of ratings for 4000 products I am
>>>>> giving numRecommendations parameter to 4000 and maxPrefsPerUser to 4000.
>>> Should
>>>>> it give 4000 items in the list for each user ? For some reasons
>>>>> the output for items which are having lower ratings is not
>>>>> displayed. I
>>> see
>>>>> the default limit is 10.
>>>>>
>>>>> I am not sure if I am not getting ratings for 4000 items because I
>>>>> am passing the wrong options for the mahout version or is it an
>>>>> issue
>>> with
>>>>> mahout ver 0.7. I am using 0.7 -mahout-examples-0.7-cdh4.3.1.jar .
>>>>>
>>>>> I see the parameter name changed in the latest version I checked
>>>>> from
>>> git
>>>>> - 0.9-SNAPSHOT
>>>>>
>>>>> maxPrefsPerUserConsidered =
>>> jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED,
>>>>> DEFAULT_MAX_PREFS_PER_USER_CONSIDERED);
>>>>>
>>>>> Will using a latest version help ?
>>>>>
>>>>>
>>>>>
>>>>>
>>>>>
>>>>> On Sun, Sep 29, 2013 at 12:29 PM, Martin, Nick <Ni...@pssd.com>
>>> wrote:
>>>>>
>>>>>> There should be a score after each recommended item (i.e.
>>>>>> 123456:2.6)
>>> in
>>>>>> your output. Lower scores would be the ones you're interested in.
>>>>>>
>>>>>> Sent from my iPhone
>>>>>>
>>>>>> On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <
>>>>>> deepak.subhramanian@gmail.com> wrote:
>>>>>>
>>>>>>> Hi
>>>>>>>
>>>>>>> I am trying to predict the ratings for some items for some users
>>> using
>>>>>> item
>>>>>>> based collaborative filtering. I tried using the mahout
>>>>>> recommenditembased
>>>>>>> , but it shows only the top 10 items or I can increase it by
>>>>>>> passing
>>> the
>>>>>>> --numRecommendations parameter. But it doesnt shows items which
>>>>>>> has
>>>>>> lower
>>>>>>> predicted rating . What is the best approach to get ratings for
>>>>>>> items
>>>>>> which
>>>>>>> has low predicted rating ?
>>>>>>>
>>>>>>>
>>>>>>> I tried this command.
>>>>>>>
>>>>>>> mahout recommenditembased --input mahoutrecoinput --usersFile
>>>>>>> recouserlist --itemsFile recoitemlist --output
>>>>>>> /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
>>>>>>> --numRecommendations 4000 --maxPrefsPerUser 4000
>>>>>>>
>>>>>>> Also I tried using the estimatePreference method on the recommender.
>>>>>>>
>>>>>>> Please help .
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> Deepak Subhramanian
>>>>
>>>>
>>>>
>>>> --
>>>> Deepak Subhramanian
>>>
>>
>>
>>
>> --
>> Deepak Subhramanian
>>
>
>
>
> --
> Deepak Subhramanian
>
RE: Getting rating for all the files
Posted by "Martin, Nick" <Ni...@pssd.com>.
Hi all,
I have the same question as Deepak does below...where can I find the User based recommender via Mahout command line?
I don't see it listed in the valid program names:
Valid program names are:
arff.vector: : Generate Vectors from an ARFF file or directory
baumwelch: : Baum-Welch algorithm for unsupervised HMM training
canopy: : Canopy clustering
cat: : Print a file or resource as the logistic regression models would see it
cleansvd: : Cleanup and verification of SVD output
clusterdump: : Dump cluster output to text
clusterpp: : Groups Clustering Output In Clusters
cmdump: : Dump confusion matrix in HTML or text formats
cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
dirichlet: : Dirichlet Clustering
eigencuts: : Eigencuts spectral clustering
evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes
fkmeans: : Fuzzy K-means clustering
fpg: : Frequent Pattern Growth
hmmpredict: : Generate random sequence of observations by given HMM
itemsimilarity: : Compute the item-item-similarities for item-based collaborative filtering
kmeans: : K-means clustering
lucene.vector: : Generate Vectors from a Lucene index
matrixdump: : Dump matrix in CSV format
matrixmult: : Take the product of two matrices
meanshift: : Mean Shift clustering
minhash: : Run Minhash clustering
parallelALS: : ALS-WR factorization of a rating matrix
recommendfactorized: : Compute recommendations using the factorization of a rating matrix
recommenditembased: : Compute recommendations using item-based collaborative filtering
regexconverter: : Convert text files on a per line basis based on regular expressions
rowid: : Map SequenceFile<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>}
rowsimilarity: : Compute the pairwise similarities of the rows of a matrix
runAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression model
runlogistic: : Run a logistic regression model against CSV data
seq2encoded: : Encoded Sparse Vector generation from Text sequence files
seq2sparse: : Sparse Vector generation from Text sequence files
seqdirectory: : Generate sequence files (of Text) from a directory
seqdumper: : Generic Sequence File dumper
seqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archives
seqwiki: : Wikipedia xml dump to sequence file
spectralkmeans: : Spectral k-means clustering
split: : Split Input data into test and train sets
splitDataset: : split a rating dataset into training and probe parts
ssvd: : Stochastic SVD
svd: : Lanczos Singular Value Decomposition
testnb: : Test the Vector-based Bayes classifier
trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model
trainlogistic: : Train a logistic regression using stochastic gradient descent
trainnb: : Train the Vector-based Bayes classifier
transpose: : Take the transpose of a matrix
validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data set
vecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectors
vectordump: : Dump vectors from a sequence file to text
viterbi: : Viterbi decoding of hidden states from given output states sequence
-----Original Message-----
From: Deepak Subhramanian [mailto:deepak.subhramanian@gmail.com]
Sent: Sunday, September 29, 2013 4:06 PM
To: user@mahout.apache.org
Subject: Re: Getting rating for all the files
I tried writing a UserRecommendation program in java. But it give me less results than the ItemBasedRecommendation. Anyone else have any thoughts on my previous question ?
On Sun, Sep 29, 2013 at 7:24 PM, Deepak Subhramanian < deepak.subhramanian@gmail.com> wrote:
> Thanks Nick. I am planning to give a try with userbasedrecommendation
> since there are low no of users. I dont see recommenduserbased option
> in the commandline utility for Mahout. Does that mean I have to write
> a Java Program to use the UserBasedRecommender ?
>
>
> On Sun, Sep 29, 2013 at 7:22 PM, Martin, Nick <Ni...@pssd.com> wrote:
>
>> I'l need to defer to one of the other math whizzes on the potential
>> reasons for recommendations for certain users not appearing. My
>> suspicion is that you would either not have sufficient co-occurrence
>> of specific users/items to support a recommendation or you may need
>> to experiment with a different similarity measure.
>>
>> Anyone else want to weigh in?
>>
>>
>>
>> Sent from my iPhone
>>
>> On Sep 29, 2013, at 1:14 PM, "Deepak Subhramanian" <
>> deepak.subhramanian@gmail.com> wrote:
>>
>> > Sorry . My mistake . I am getting the lower ratings for some of the
>> users
>> > and items. But my issue is not solved . I am not getting ratings
>> > for
>> some
>> > of the users and some of the ratings.
>> >
>> > My userFile has 8000 users and my itemsFile has 4000 Items . But I
>> > get recommendations for only 5000 users and 1500 items. And the
>> > maximum no
>> of
>> > recommendations given is 258. What can be the reasons that there
>> > is no items recommendations for 3000 users and 2500 items. Is it
>> > because
>> there is
>> > no similarities exist between those users and items ?
>> >
>> >
>> > On Sun, Sep 29, 2013 at 4:46 PM, Deepak Subhramanian <
>> > deepak.subhramanian@gmail.com> wrote:
>> >
>> >> Thanks Nick. As I mentioned earleir I am getting ratings only for
>> >> the
>> top
>> >> recommended products instead of ratings for 4000 products I am
>> >> giving numRecommendations parameter to 4000 and maxPrefsPerUser to 4000.
>> Should
>> >> it give 4000 items in the list for each user ? For some reasons
>> >> the output for items which are having lower ratings is not
>> >> displayed. I
>> see
>> >> the default limit is 10.
>> >>
>> >> I am not sure if I am not getting ratings for 4000 items because I
>> >> am passing the wrong options for the mahout version or is it an
>> >> issue
>> with
>> >> mahout ver 0.7. I am using 0.7 -mahout-examples-0.7-cdh4.3.1.jar .
>> >>
>> >> I see the parameter name changed in the latest version I checked
>> >> from
>> git
>> >> - 0.9-SNAPSHOT
>> >>
>> >> maxPrefsPerUserConsidered =
>> jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED,
>> >> DEFAULT_MAX_PREFS_PER_USER_CONSIDERED);
>> >>
>> >> Will using a latest version help ?
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> On Sun, Sep 29, 2013 at 12:29 PM, Martin, Nick <Ni...@pssd.com>
>> wrote:
>> >>
>> >>> There should be a score after each recommended item (i.e.
>> >>> 123456:2.6)
>> in
>> >>> your output. Lower scores would be the ones you're interested in.
>> >>>
>> >>> Sent from my iPhone
>> >>>
>> >>> On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <
>> >>> deepak.subhramanian@gmail.com> wrote:
>> >>>
>> >>>> Hi
>> >>>>
>> >>>> I am trying to predict the ratings for some items for some users
>> using
>> >>> item
>> >>>> based collaborative filtering. I tried using the mahout
>> >>> recommenditembased
>> >>>> , but it shows only the top 10 items or I can increase it by
>> >>>> passing
>> the
>> >>>> --numRecommendations parameter. But it doesnt shows items which
>> >>>> has
>> >>> lower
>> >>>> predicted rating . What is the best approach to get ratings for
>> >>>> items
>> >>> which
>> >>>> has low predicted rating ?
>> >>>>
>> >>>>
>> >>>> I tried this command.
>> >>>>
>> >>>> mahout recommenditembased --input mahoutrecoinput --usersFile
>> >>>> recouserlist --itemsFile recoitemlist --output
>> >>>> /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
>> >>>> --numRecommendations 4000 --maxPrefsPerUser 4000
>> >>>>
>> >>>> Also I tried using the estimatePreference method on the recommender.
>> >>>>
>> >>>> Please help .
>> >>
>> >>
>> >>
>> >> --
>> >> Deepak Subhramanian
>> >
>> >
>> >
>> > --
>> > Deepak Subhramanian
>>
>
>
>
> --
> Deepak Subhramanian
>
--
Deepak Subhramanian
Re: Getting rating for all the files
Posted by Deepak Subhramanian <de...@gmail.com>.
I tried writing a UserRecommendation program in java. But it give me less
results than the ItemBasedRecommendation. Anyone else have any thoughts on
my previous question ?
On Sun, Sep 29, 2013 at 7:24 PM, Deepak Subhramanian <
deepak.subhramanian@gmail.com> wrote:
> Thanks Nick. I am planning to give a try with userbasedrecommendation
> since there are low no of users. I dont see recommenduserbased option in
> the commandline utility for Mahout. Does that mean I have to write a Java
> Program to use the UserBasedRecommender ?
>
>
> On Sun, Sep 29, 2013 at 7:22 PM, Martin, Nick <Ni...@pssd.com> wrote:
>
>> I'l need to defer to one of the other math whizzes on the potential
>> reasons for recommendations for certain users not appearing. My suspicion
>> is that you would either not have sufficient co-occurrence of specific
>> users/items to support a recommendation or you may need to experiment with
>> a different similarity measure.
>>
>> Anyone else want to weigh in?
>>
>>
>>
>> Sent from my iPhone
>>
>> On Sep 29, 2013, at 1:14 PM, "Deepak Subhramanian" <
>> deepak.subhramanian@gmail.com> wrote:
>>
>> > Sorry . My mistake . I am getting the lower ratings for some of the
>> users
>> > and items. But my issue is not solved . I am not getting ratings for
>> some
>> > of the users and some of the ratings.
>> >
>> > My userFile has 8000 users and my itemsFile has 4000 Items . But I get
>> > recommendations for only 5000 users and 1500 items. And the maximum no
>> of
>> > recommendations given is 258. What can be the reasons that there is no
>> > items recommendations for 3000 users and 2500 items. Is it because
>> there is
>> > no similarities exist between those users and items ?
>> >
>> >
>> > On Sun, Sep 29, 2013 at 4:46 PM, Deepak Subhramanian <
>> > deepak.subhramanian@gmail.com> wrote:
>> >
>> >> Thanks Nick. As I mentioned earleir I am getting ratings only for the
>> top
>> >> recommended products instead of ratings for 4000 products I am giving
>> >> numRecommendations parameter to 4000 and maxPrefsPerUser to 4000.
>> Should
>> >> it give 4000 items in the list for each user ? For some reasons the
>> >> output for items which are having lower ratings is not displayed. I
>> see
>> >> the default limit is 10.
>> >>
>> >> I am not sure if I am not getting ratings for 4000 items because I am
>> >> passing the wrong options for the mahout version or is it an issue
>> with
>> >> mahout ver 0.7. I am using 0.7 -mahout-examples-0.7-cdh4.3.1.jar .
>> >>
>> >> I see the parameter name changed in the latest version I checked from
>> git
>> >> - 0.9-SNAPSHOT
>> >>
>> >> maxPrefsPerUserConsidered =
>> jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED,
>> >> DEFAULT_MAX_PREFS_PER_USER_CONSIDERED);
>> >>
>> >> Will using a latest version help ?
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> On Sun, Sep 29, 2013 at 12:29 PM, Martin, Nick <Ni...@pssd.com>
>> wrote:
>> >>
>> >>> There should be a score after each recommended item (i.e. 123456:2.6)
>> in
>> >>> your output. Lower scores would be the ones you're interested in.
>> >>>
>> >>> Sent from my iPhone
>> >>>
>> >>> On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <
>> >>> deepak.subhramanian@gmail.com> wrote:
>> >>>
>> >>>> Hi
>> >>>>
>> >>>> I am trying to predict the ratings for some items for some users
>> using
>> >>> item
>> >>>> based collaborative filtering. I tried using the mahout
>> >>> recommenditembased
>> >>>> , but it shows only the top 10 items or I can increase it by passing
>> the
>> >>>> --numRecommendations parameter. But it doesnt shows items which has
>> >>> lower
>> >>>> predicted rating . What is the best approach to get ratings for items
>> >>> which
>> >>>> has low predicted rating ?
>> >>>>
>> >>>>
>> >>>> I tried this command.
>> >>>>
>> >>>> mahout recommenditembased --input mahoutrecoinput --usersFile
>> >>>> recouserlist --itemsFile recoitemlist --output
>> >>>> /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
>> >>>> --numRecommendations 4000 --maxPrefsPerUser 4000
>> >>>>
>> >>>> Also I tried using the estimatePreference method on the recommender.
>> >>>>
>> >>>> Please help .
>> >>
>> >>
>> >>
>> >> --
>> >> Deepak Subhramanian
>> >
>> >
>> >
>> > --
>> > Deepak Subhramanian
>>
>
>
>
> --
> Deepak Subhramanian
>
--
Deepak Subhramanian
Re: Getting rating for all the files
Posted by Deepak Subhramanian <de...@gmail.com>.
Thanks Nick. I am planning to give a try with userbasedrecommendation since
there are low no of users. I dont see recommenduserbased option in the
commandline utility for Mahout. Does that mean I have to write a Java
Program to use the UserBasedRecommender ?
On Sun, Sep 29, 2013 at 7:22 PM, Martin, Nick <Ni...@pssd.com> wrote:
> I'l need to defer to one of the other math whizzes on the potential
> reasons for recommendations for certain users not appearing. My suspicion
> is that you would either not have sufficient co-occurrence of specific
> users/items to support a recommendation or you may need to experiment with
> a different similarity measure.
>
> Anyone else want to weigh in?
>
>
>
> Sent from my iPhone
>
> On Sep 29, 2013, at 1:14 PM, "Deepak Subhramanian" <
> deepak.subhramanian@gmail.com> wrote:
>
> > Sorry . My mistake . I am getting the lower ratings for some of the users
> > and items. But my issue is not solved . I am not getting ratings for some
> > of the users and some of the ratings.
> >
> > My userFile has 8000 users and my itemsFile has 4000 Items . But I get
> > recommendations for only 5000 users and 1500 items. And the maximum no
> of
> > recommendations given is 258. What can be the reasons that there is no
> > items recommendations for 3000 users and 2500 items. Is it because there
> is
> > no similarities exist between those users and items ?
> >
> >
> > On Sun, Sep 29, 2013 at 4:46 PM, Deepak Subhramanian <
> > deepak.subhramanian@gmail.com> wrote:
> >
> >> Thanks Nick. As I mentioned earleir I am getting ratings only for the
> top
> >> recommended products instead of ratings for 4000 products I am giving
> >> numRecommendations parameter to 4000 and maxPrefsPerUser to 4000.
> Should
> >> it give 4000 items in the list for each user ? For some reasons the
> >> output for items which are having lower ratings is not displayed. I see
> >> the default limit is 10.
> >>
> >> I am not sure if I am not getting ratings for 4000 items because I am
> >> passing the wrong options for the mahout version or is it an issue with
> >> mahout ver 0.7. I am using 0.7 -mahout-examples-0.7-cdh4.3.1.jar .
> >>
> >> I see the parameter name changed in the latest version I checked from
> git
> >> - 0.9-SNAPSHOT
> >>
> >> maxPrefsPerUserConsidered =
> jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED,
> >> DEFAULT_MAX_PREFS_PER_USER_CONSIDERED);
> >>
> >> Will using a latest version help ?
> >>
> >>
> >>
> >>
> >>
> >> On Sun, Sep 29, 2013 at 12:29 PM, Martin, Nick <Ni...@pssd.com>
> wrote:
> >>
> >>> There should be a score after each recommended item (i.e. 123456:2.6)
> in
> >>> your output. Lower scores would be the ones you're interested in.
> >>>
> >>> Sent from my iPhone
> >>>
> >>> On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <
> >>> deepak.subhramanian@gmail.com> wrote:
> >>>
> >>>> Hi
> >>>>
> >>>> I am trying to predict the ratings for some items for some users using
> >>> item
> >>>> based collaborative filtering. I tried using the mahout
> >>> recommenditembased
> >>>> , but it shows only the top 10 items or I can increase it by passing
> the
> >>>> --numRecommendations parameter. But it doesnt shows items which has
> >>> lower
> >>>> predicted rating . What is the best approach to get ratings for items
> >>> which
> >>>> has low predicted rating ?
> >>>>
> >>>>
> >>>> I tried this command.
> >>>>
> >>>> mahout recommenditembased --input mahoutrecoinput --usersFile
> >>>> recouserlist --itemsFile recoitemlist --output
> >>>> /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
> >>>> --numRecommendations 4000 --maxPrefsPerUser 4000
> >>>>
> >>>> Also I tried using the estimatePreference method on the recommender.
> >>>>
> >>>> Please help .
> >>
> >>
> >>
> >> --
> >> Deepak Subhramanian
> >
> >
> >
> > --
> > Deepak Subhramanian
>
--
Deepak Subhramanian
Re: Getting rating for all the files
Posted by "Martin, Nick" <Ni...@pssd.com>.
I'l need to defer to one of the other math whizzes on the potential reasons for recommendations for certain users not appearing. My suspicion is that you would either not have sufficient co-occurrence of specific users/items to support a recommendation or you may need to experiment with a different similarity measure.
Anyone else want to weigh in?
Sent from my iPhone
On Sep 29, 2013, at 1:14 PM, "Deepak Subhramanian" <de...@gmail.com> wrote:
> Sorry . My mistake . I am getting the lower ratings for some of the users
> and items. But my issue is not solved . I am not getting ratings for some
> of the users and some of the ratings.
>
> My userFile has 8000 users and my itemsFile has 4000 Items . But I get
> recommendations for only 5000 users and 1500 items. And the maximum no of
> recommendations given is 258. What can be the reasons that there is no
> items recommendations for 3000 users and 2500 items. Is it because there is
> no similarities exist between those users and items ?
>
>
> On Sun, Sep 29, 2013 at 4:46 PM, Deepak Subhramanian <
> deepak.subhramanian@gmail.com> wrote:
>
>> Thanks Nick. As I mentioned earleir I am getting ratings only for the top
>> recommended products instead of ratings for 4000 products I am giving
>> numRecommendations parameter to 4000 and maxPrefsPerUser to 4000. Should
>> it give 4000 items in the list for each user ? For some reasons the
>> output for items which are having lower ratings is not displayed. I see
>> the default limit is 10.
>>
>> I am not sure if I am not getting ratings for 4000 items because I am
>> passing the wrong options for the mahout version or is it an issue with
>> mahout ver 0.7. I am using 0.7 -mahout-examples-0.7-cdh4.3.1.jar .
>>
>> I see the parameter name changed in the latest version I checked from git
>> - 0.9-SNAPSHOT
>>
>> maxPrefsPerUserConsidered = jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED,
>> DEFAULT_MAX_PREFS_PER_USER_CONSIDERED);
>>
>> Will using a latest version help ?
>>
>>
>>
>>
>>
>> On Sun, Sep 29, 2013 at 12:29 PM, Martin, Nick <Ni...@pssd.com> wrote:
>>
>>> There should be a score after each recommended item (i.e. 123456:2.6) in
>>> your output. Lower scores would be the ones you're interested in.
>>>
>>> Sent from my iPhone
>>>
>>> On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <
>>> deepak.subhramanian@gmail.com> wrote:
>>>
>>>> Hi
>>>>
>>>> I am trying to predict the ratings for some items for some users using
>>> item
>>>> based collaborative filtering. I tried using the mahout
>>> recommenditembased
>>>> , but it shows only the top 10 items or I can increase it by passing the
>>>> --numRecommendations parameter. But it doesnt shows items which has
>>> lower
>>>> predicted rating . What is the best approach to get ratings for items
>>> which
>>>> has low predicted rating ?
>>>>
>>>>
>>>> I tried this command.
>>>>
>>>> mahout recommenditembased --input mahoutrecoinput --usersFile
>>>> recouserlist --itemsFile recoitemlist --output
>>>> /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
>>>> --numRecommendations 4000 --maxPrefsPerUser 4000
>>>>
>>>> Also I tried using the estimatePreference method on the recommender.
>>>>
>>>> Please help .
>>
>>
>>
>> --
>> Deepak Subhramanian
>
>
>
> --
> Deepak Subhramanian
Re: Getting rating for all the files
Posted by Deepak Subhramanian <de...@gmail.com>.
Sorry . My mistake . I am getting the lower ratings for some of the users
and items. But my issue is not solved . I am not getting ratings for some
of the users and some of the ratings.
My userFile has 8000 users and my itemsFile has 4000 Items . But I get
recommendations for only 5000 users and 1500 items. And the maximum no of
recommendations given is 258. What can be the reasons that there is no
items recommendations for 3000 users and 2500 items. Is it because there is
no similarities exist between those users and items ?
On Sun, Sep 29, 2013 at 4:46 PM, Deepak Subhramanian <
deepak.subhramanian@gmail.com> wrote:
> Thanks Nick. As I mentioned earleir I am getting ratings only for the top
> recommended products instead of ratings for 4000 products I am giving
> numRecommendations parameter to 4000 and maxPrefsPerUser to 4000. Should
> it give 4000 items in the list for each user ? For some reasons the
> output for items which are having lower ratings is not displayed. I see
> the default limit is 10.
>
> I am not sure if I am not getting ratings for 4000 items because I am
> passing the wrong options for the mahout version or is it an issue with
> mahout ver 0.7. I am using 0.7 -mahout-examples-0.7-cdh4.3.1.jar .
>
> I see the parameter name changed in the latest version I checked from git
> - 0.9-SNAPSHOT
>
> maxPrefsPerUserConsidered = jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED,
> DEFAULT_MAX_PREFS_PER_USER_CONSIDERED);
>
> Will using a latest version help ?
>
>
>
>
>
> On Sun, Sep 29, 2013 at 12:29 PM, Martin, Nick <Ni...@pssd.com> wrote:
>
>> There should be a score after each recommended item (i.e. 123456:2.6) in
>> your output. Lower scores would be the ones you're interested in.
>>
>> Sent from my iPhone
>>
>> On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <
>> deepak.subhramanian@gmail.com> wrote:
>>
>> > Hi
>> >
>> > I am trying to predict the ratings for some items for some users using
>> item
>> > based collaborative filtering. I tried using the mahout
>> recommenditembased
>> > , but it shows only the top 10 items or I can increase it by passing the
>> > --numRecommendations parameter. But it doesnt shows items which has
>> lower
>> > predicted rating . What is the best approach to get ratings for items
>> which
>> > has low predicted rating ?
>> >
>> >
>> > I tried this command.
>> >
>> > mahout recommenditembased --input mahoutrecoinput --usersFile
>> > recouserlist --itemsFile recoitemlist --output
>> > /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
>> > --numRecommendations 4000 --maxPrefsPerUser 4000
>> >
>> > Also I tried using the estimatePreference method on the recommender.
>> >
>> > Please help .
>>
>
>
>
> --
> Deepak Subhramanian
>
--
Deepak Subhramanian
Re: Getting rating for all the files
Posted by Deepak Subhramanian <de...@gmail.com>.
Thanks Nick. As I mentioned earleir I am getting ratings only for the top
recommended products instead of ratings for 4000 products I am giving
numRecommendations parameter to 4000 and maxPrefsPerUser to 4000. Should
it give 4000 items in the list for each user ? For some reasons the output
for items which are having lower ratings is not displayed. I see the
default limit is 10.
I am not sure if I am not getting ratings for 4000 items because I am
passing the wrong options for the mahout version or is it an issue with
mahout ver 0.7. I am using 0.7 -mahout-examples-0.7-cdh4.3.1.jar .
I see the parameter name changed in the latest version I checked from git
- 0.9-SNAPSHOT
maxPrefsPerUserConsidered = jobConf.getInt(MAX_PREFS_PER_USER_CONSIDERED,
DEFAULT_MAX_PREFS_PER_USER_CONSIDERED);
Will using a latest version help ?
On Sun, Sep 29, 2013 at 12:29 PM, Martin, Nick <Ni...@pssd.com> wrote:
> There should be a score after each recommended item (i.e. 123456:2.6) in
> your output. Lower scores would be the ones you're interested in.
>
> Sent from my iPhone
>
> On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <
> deepak.subhramanian@gmail.com> wrote:
>
> > Hi
> >
> > I am trying to predict the ratings for some items for some users using
> item
> > based collaborative filtering. I tried using the mahout
> recommenditembased
> > , but it shows only the top 10 items or I can increase it by passing the
> > --numRecommendations parameter. But it doesnt shows items which has lower
> > predicted rating . What is the best approach to get ratings for items
> which
> > has low predicted rating ?
> >
> >
> > I tried this command.
> >
> > mahout recommenditembased --input mahoutrecoinput --usersFile
> > recouserlist --itemsFile recoitemlist --output
> > /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
> > --numRecommendations 4000 --maxPrefsPerUser 4000
> >
> > Also I tried using the estimatePreference method on the recommender.
> >
> > Please help .
>
--
Deepak Subhramanian
Re: Getting rating for all the files
Posted by "Martin, Nick" <Ni...@pssd.com>.
There should be a score after each recommended item (i.e. 123456:2.6) in your output. Lower scores would be the ones you're interested in.
Sent from my iPhone
On Sep 28, 2013, at 8:25 AM, "Deepak Subhramanian" <de...@gmail.com> wrote:
> Hi
>
> I am trying to predict the ratings for some items for some users using item
> based collaborative filtering. I tried using the mahout recommenditembased
> , but it shows only the top 10 items or I can increase it by passing the
> --numRecommendations parameter. But it doesnt shows items which has lower
> predicted rating . What is the best approach to get ratings for items which
> has low predicted rating ?
>
>
> I tried this command.
>
> mahout recommenditembased --input mahoutrecoinput --usersFile
> recouserlist --itemsFile recoitemlist --output
> /mahoutrecooutputpearsonnew -s SIMILARITY_PEARSON_CORRELATION
> --numRecommendations 4000 --maxPrefsPerUser 4000
>
> Also I tried using the estimatePreference method on the recommender.
>
> Please help .