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:[email protected]]
Sent: Sunday, September 29, 2013 4:06 PM
To: [email protected]
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 <
[email protected]> 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 <[email protected]> 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" <
>> [email protected]> 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 <
>> > [email protected]> 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 <[email protected]>
>> 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" <
>> >>> [email protected]> 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
