The output of parallelALS are two matrices U and M whose product is an
approximation of your input matrix.
The matrices are outputed as sequence files with an IntWritable as key
(the index of the row in the matrix) and a VectorWritable as value which
holds the contents of the row vector.
Sean Owen srowen at gmail.com writes:
Parallel ALS is exactly an example of where you can use matrix
factorization for 0/1 data.
On Mon, May 6, 2013 at 9:22 PM, Tevfik Aytekin tevfik.aytekin at
gmail.com wrote:
Hi Sean,
Isn't boolean preferences is supported in the context of
Ted Dunning ted.dunning at gmail.com writes:
WIthout more information it is impossible to comment.
What experiments?
On Fri, May 3, 2013 at 8:45 AM, William icswilliam2010 at gmail.com
wrote:
I'm trying to get some recommendations with three Algorithms:
1.parallelALS
If you have no ratings, how are you using RMSE? this typically
measures error in reconstructing ratings.
I think you are probably measuring something meaningless.
On Mon, May 6, 2013 at 10:17 AM, William icswilliam2...@gmail.com wrote:
I have a dataset about user and movie(no rate).But I want to
ALS-WR weights the error on each term differently, so the average
error doesn't really have meaning here, even if you are comparing the
difference with 1. I think you will need to fall back to mean
average precision or something.
On Mon, May 6, 2013 at 11:24 AM, William icswilliam2...@gmail.com
This problem is called one-class classification problem. In the domain
of collaborative filtering it is called one-class collaborative
filtering (since what you have are only positive preferences). You may
search the web with these key words to find papers providing
solutions. I'm not sure whether
Yes, it goes by the name 'boolean prefs' in the project since target
variables don't have values -- they just exist or don't.
So, yes it's certainly supported but the question here is how to
evaluate the output.
On Mon, May 6, 2013 at 8:29 PM, Tevfik Aytekin tevfik.ayte...@gmail.com wrote:
This
Hi Sean,
Isn't boolean preferences is supported in the context of memory-based
recommendation algorithms in Mahout?
Are there matrix factorization algorithms in Mahout which can work
with this kind of data (that is, the kind of data which consists of
users and the movies they have seen).
On
Parallel ALS is exactly an example of where you can use matrix
factorization for 0/1 data.
On Mon, May 6, 2013 at 9:22 PM, Tevfik Aytekin tevfik.ayte...@gmail.com wrote:
Hi Sean,
Isn't boolean preferences is supported in the context of memory-based
recommendation algorithms in Mahout?
Are
But the data under consideration here is not 0/1 data, it contains only 1's.
On Mon, May 6, 2013 at 11:29 PM, Sean Owen sro...@gmail.com wrote:
Parallel ALS is exactly an example of where you can use matrix
factorization for 0/1 data.
On Mon, May 6, 2013 at 9:22 PM, Tevfik Aytekin
Yes, that's really what I mean. ALS factors, among other things, a
matrix of 1 where an interaction occurs and nothing (implicitly 0)
everywhere else.
On Mon, May 6, 2013 at 9:40 PM, Tevfik Aytekin tevfik.ayte...@gmail.com wrote:
But the data under consideration here is not 0/1 data, it contains
I'm trying to get some recommendations with three Algorithms:
1.parallelALS
2.evaluateFactorization
3.recommendfactorized
In my experiments, RMSE value monotonically increases with larger
numfeatures.
But Base on Netflix Prize experiment, RMSE should decreases with larger
numfeatures.
How to
WIthout more information it is impossible to comment.
What experiments?
On Fri, May 3, 2013 at 8:45 AM, William icswilliam2...@gmail.com wrote:
I'm trying to get some recommendations with three Algorithms:
1.parallelALS
2.evaluateFactorization
3.recommendfactorized
In my experiments,
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