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.subhraman...@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.subhraman...@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 <nimar...@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.subhraman...@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.subhraman...@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 <nimar...@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.subhraman...@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