Hi, Brian,
this question is also relevant for me. Perhaps somebody will give more
details because I am just learning myself. But, I guess you can try to
change the parameters, and check the performance, and write here about it
that everybody would get more knowledge!
In general, if these values
So I'm using predictFromFactorization in Mahout 0.5 but this code was
removed from 0.6. Is there any special reason for this?
Thanks
Stuart
On 12 September 2013 08:28, Stuart Horsman stuart.hors...@gmail.com wrote:
Hi All,
I'm new to mahout so thanks up front for the help. I'm running
Hi Brian * *Miliauskas,
I am a data mining engineer form Taobao recommendation team. In past one
month, I have read all the code of mahout itemCF.
So maybe I can answer this question.
We consider the input of itemCF for one user is a item vector, like this
(the notation is from Json object
Hi
I have found the following thread about adding new data points/documents
to existing clusters, without having to run the clustering again
http://lucene.472066.n3.nabble.com/Updating-clusters-td972794.html
Grant describes that one possibility is to check which cluster thew data
Dear All,
I am trying to use SparseVectorsFromSequenceFiles () through Java code
(NetBeans 7Windows 7) . here is my code (API):
//inputPath is the path of my SequenceFile
Path inputPath = new Path(C:\\Users\\DARIUS\\forTest1.txt);
//outputPath where I expect some results
Path outputPath = new
Hi,
Thank you for the response! What you said makes sense. Here is a link to
the other property:
http://grepcode.com/file/repo1.maven.org/maven2/org.apache.mahout/mahout-core/0.6/org/apache/mahout/cf/taste/hadoop/item/RecommenderJob.java#RecommenderJob.0DEFAULT_MAX_SIMILARITIES_PER_ITEM
Hi Brain,
The parameter maxPrefsPerUserInItemSimilarity is in RecommenderJob, from
the text of comment, It is the same as the paramter maxPrefsPerUser in
ItemSimilarityJob.
The second question is not easy to answer. It is decided by your
recommendation scenario and input data features. The
Hi Brian,
Happy to give you some details:
So, from a matrix A (user x item) that holds user-item interactions,
this algorithm first computes a matrix S (item x item) of item
similarities and afterwards uses these item similarities to compute
recommendations for users.
the parameters refer to the
Hi,
I'd like to use Mahout for clustering and classification where I have tens of
terabytes of data on Amazon's S3 storage service. Each file in my data will
generate one data point where I need to decompress the file and process it
prior to applying machine learning. Is it necessary to have
Although Windows is not officially supported, your
svsf.run(new String[]{inputPath.toString(), outputPath.toString()})
should be
svsf.run(new String[]{-i,inputPath.toString(), -o,
outputPath.toString()}) anyway.
Best
Gokhan
On Thu, Sep 12, 2013 at 4:14 PM, Darius Miliauskas
Hi Parnab,
When running lda using commandline cvb utility, you may pass -o option for
the output path for topic-term distributions, and -dt option for the output
path for doc-topic distributions.
Hope that helps.
Best
Gokhan
On Wed, Sep 11, 2013 at 11:38 PM, parnab kumar
Hi Stevo,
So the method predictFromFactorization, which was in PredictorJob, seems
not to have been migrated over. RecommenderJob gives me top N
recommendations (plus predicted preferences). predictFromFactorization is
handy because I can pass in userid, itemid pairs and get a preference
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