I overlooked that you're interested in document similarities. Sry again :)
Another way would be to read the output of RowSimilarityJob with a
o.a.m.common.iterator.sequencefile.SequenceFileDirIterable
You create a list of instances of
o.a.m.cf.taste.impl.similarity.GenericItemSimilarity.ItemItemSimilarity
e.g. for the output
Key: 0: Value: {61112:0.21139380179557016,52144:0.23797846026935565,...}
you would do
list.add(new ItemItemSimilarity(0, 61112, 0.21139380179557016));
list.add(new ItemItemSimilarity(0, 52144, 0.23797846026935565));
...
After that you create a GenericItemSimilarity from the list of
ItemItemSimilarities, which is the in-memory item similarity you asked for.
Hope that helps,
Sebastian
On 02/24/2014 10:04 PM, Juan José Ramos wrote:
Correct me if I'm wrong, but is it not the ItemSimilarityJob mean to be for
item-based CF? In particular, in the documentation I can read that:
Preferences in the input file should look like
userID,itemID[,preferencevalue]
And in my case the input I have is just text documents and I want to
pre-compute similarities between them beforehand, even before any user has
expressed any preference value for any item.
In order to use ItemSimilarityJob for this purpose, what should be the
input I need to provide? Would it be the output of seq2sparse?
Thanks again.
On Mon, Feb 24, 2014 at 8:54 PM, Sebastian Schelter <[email protected]> wrote:
You're right, my bad. If you don't use RowSimilarityJob directly, but
org.apache.mahout.cf.taste.hadoop.similarity.item.ItemSimilarityJob
(which calls RowSimilarityJob under the covers), your output will be a
textfile that is directly usable with FileItemSimilarity.
--sebastian
On 02/24/2014 09:30 PM, Juan José Ramos wrote:
Thanks for the prompt reply.
RowSimilarityJob produces an output in the form of:
Key: 0: Value: {61112:0.21139380179557016,52144:0.23797846026935565,...}
whereas FileItemSimilarity is expecting a comma or tab separated inputs.
I assume that you meant that the output of RowSimilarityJob can be loaded
by the FileItemSimilarity after doing the appropriate parsing. Is that
correct, or is there actually a way to load the raw output of
RowSimilarityJob into FileItemSimilarity?
Thanks.
On Mon, Feb 24, 2014 at 7:41 PM, Sebastian Schelter <[email protected]>
wrote:
The output of RowSimilarityJob can be loaded by the FileItemSimilarity.
--sebastian
On 02/24/2014 08:31 PM, Juan José Ramos wrote:
Is there a way to reproduce this process:
https://cwiki.apache.org/confluence/display/MAHOUT/
Quick+tour+of+text+analysis+using+the+Mahout+command+line
inside Java code and not using the command line tool? I am not
interested
in the clustering part but in 'Calculate several similar docs to each
doc
in the data'. In particular, I am interested in loading the output of
the
rowsimilarity tool into memory to be used as my custom ItemSimilarity
implementation for an ItemBasedRecommender.
What I exactly want is to have a matrix in memory where for every doc in
my
catalogue I have the similarity with the 100 (that is the threshold I am
using) most similar items an undefined similarity for the rest.
Is it possible to do with the Java API? I know it can be done calling
the
commands from inside the Java code and I guess that also using
corresponding SparseVectorsFromSequenceFiles, DistributedRowMatrix and
RowItemSimilarityJob. But I still see cannot see an easy way of parsing
the
output of RowItemSimilarityJob to the memory representation I intend to
use.
Thanks a lot.
