Yes, I know that RMSE and offline evaluation have many drawbacks, but I
just need to compute it.
Yes, I need to implement a lookup function, I was wondering which is the
easiest way, since I am not a Java programmer and I've started using Mahout
since a few days ago.
Eugenio
2015-02-15 1:10
On Sat, Feb 14, 2015 at 6:05 AM, Eugenio Tacchini
eugenio.tacch...@gmail.com wrote:
Hi Pat, I don't understand why it is not a Mahout problem, my goal is to
evaluate (RMSE) the output of a user based algorithm comparing different
user similarity measures, Mahout already has everything I need
Hi Ted; I don't have constraint, I have to compute all the distances, but
the distances are already computed, I already have a text file which tells
me the pairwise distances among all the users and I need to fill the mahout
user-based algo with these distances.
Hi Pat, I don't understand why it
I just meant that you can make recommendations with the data you have, without
using Mahout. But I see now that you are trying to use it to calculate RMSE.
And that requires Taste. I believe using it has already been described below.
It should be noted that, except for a few special cases, RMSE
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
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
Hello Pat and thanks for your reply,
I know that when users items normally item-based works better and I
don't assume my similarity metric works better but I have, for research
purposes, to compare:
- RMSE produced by a pearson correlation user-based algorithm VS
- RMSE produced by a user-based
You can create your custom class with your similarity implementation. All
you need is that class to implement the UserSimilarity interface and use it
here
UserSimilarity similarity = new PearsonCorrelationSimilarity(dm);
instead of the PearsonCorrelationSimilarity.
UserSimilarity similarity =
Yes, I know I can implement a custom user similarity but what I want to do
is passing to mahout fixed, pre-computed user similarities I have already
stored in a text file in the easiest way possible, since I am not a Java
programmer.
If there is no way to do it, I will implement
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 DataModel.
When I have done something like that in the past, you need to build your
own thing of parsing the
Hi all,
I am new to mahout but I work with recommender systems, I have just tried
to implement a simple user-based recommender:
DataModel dm = new FileDataModel(new File(data/ratings.dat));
UserSimilarity similarity = new PearsonCorrelationSimilarity(dm);
UserNeighborhood neighborhood = new
There are many algorithms in Mahout but not all are equal. Some combinations
never perform well even though they are described in Mahout in Action. The
combination below is probably not the best.
You seem to assume your user similarity metric is better than Mahout’s? Do you
have more users or
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