Hi guys,
I've successfully trained a Naive Bayes classification model, but now I want to
use this model to classify incoming documents. How can one do this using Mahout
commands?
The only working solutions I've found are to develop customer Java programs
that does all the magic for me (this
Hi
After re-running
mvn clean install
the build was successful :-)
Sorry for the noise
Michael
Am 27.08.13 15:59, schrieb Michael Wechner:
Hi
I have downloaded
http://mirror.switch.ch/mirror/apache/dist/mahout/0.8/mahout-distribution-0.8-src.zip
and tried to build it with
mvn
Hi
I am looking for a recommender example for news articles which is making
suggestions based on a user profile (independent of other users/readers)
or more specific on the reading history of a user.
Let's say a specific user likes to read articles about cycling and
international politics
Hi Michael,
Those are collaborative filtering examples, which would recommend a news
article i, to a user u, based on:
- A weighted average of other users' ratings on i (where weight is the
similarity of two users' rating histories)
- A weighted average of u's ratings on other items (where weight
Hi Darius,
There is no module called mahout-utils in mahout 0.8. The documentation you
referred to is prepared for mahout 0.4, it is outdated.
So if you want to use mahout 0.8, remove that dependency, mahout-core would
be sufficient.
But that wouldn't compile, since SlopeOneRecommender doesn't
You can use the Mahout text pipeline, which will give you weighted vectors
based on TFIDF for each article. There is an example of this in Mahout in
Action for clustering. Then run the RowSimilarityJob on them instead of
clustering. This will give you a strength of similarity for each article
Dear All,
I guess my question would be already discussed. I found a short explanation
in stackoverflow (
http://stackoverflow.com/questions/7638400/mahouts-datamodel-with-genericdatamodel)
but I would like to get more details.
1. Let's say I uploaded data from the file to DataModel. Is it the
Hi Gokhan
Thanks very much for the keywords and hints about this topic.
Will do some more research and probably come back again at some later stage.
Thanks
Michael
Am 29.08.13 16:53, schrieb Gokhan Capan:
Hi Michael,
Those are collaborative filtering examples, which would recommend a news
Hi Pat
Thanks very much for your suggestions. I will try to develop a
recommender based on that and if somebody
is interested in it, then I could contribute it as another example.
Thanks
Michael
Am 29.08.13 18:02, schrieb Pat Ferrel:
You can use the Mahout text pipeline, which will give