On Fri, Feb 13, 2015 at 9:37 AM, Eugenio Tacchini
eugenio.tacch...@gmail.com wrote:
If I need to use a classical user-based technique, however, the only
alternative is the Taste-oriented code, am I right?
Right.
Still, I can't see how
to perform a prediction for a a user/item couple, is
Ok, thanks for your support.
Eugenio
2015-02-11 11:54 GMT+01:00 Juanjo Ramos jjar...@gmail.com:
Yes. You approach sounds about right.
As far as I know, you just cannot not pass a file to Mahout with user
similarities and it will create a UserSimilarity object as it can do with
the
I am trying to add the fixed user similarities in the easiest possible way.
This is my starting code (a normal user-based algorithm based on Pearson
Correlation):
UserSimilarity similarity = new PearsonCorrelationSimilarity(dm);
UserNeighborhood neighborhood = new NearestNUserNeighborhood(15,
On Fri, Feb 13, 2015 at 11:11 AM, Eugenio Tacchini
eugenio.tacch...@gmail.com wrote:
Is there anyone who can give me some hints about this task?
Another way to look at this is to try to wedge this into the item
similarity code.
There are hooks available in the map-reduce version of item
spark-rowsimilarity will give you a list of similar users (rows in the
interaction matrix) using LLR with several downsampling options. This works
with rows for input but you can input elements with a little custom code to get
exactly the same result.
Let me understand the second part of your
If the user - similar users relationship is really fixed for some test this
isn’t even a Mahout problem… All you need to do is create a linear combination
of all the similar user's preferences and rank accordingly. This produces
ranked recs for some “current user”. If you have a record of user
Hi
Looks like this is typical everywhere, however I have'nt figured out how to
resolve in my case.
There is nothing I have done explicitly regarding SLF4J.
Both Hadoop and Mahout environment are built by just simply downloading jar
files. Not built locally.
Both Hadoop and Mahout have been
