Congrats Sebastian
Is the code for this already in Mahout trunk?
Nick
On Sun, Jul 21, 2013 at 8:22 PM, Sebastian Schelter s...@apache.org wrote:
I'm happy to anounce that a paper called Distributed Matrix
Factorization with MapReduce using a series of Broadcast-Joins written
by me and my
Congratulations~
By the way, could the paper be shared? THX~
Best,
LiuLiu
On Mon, Jul 22, 2013 at 2:22 AM, Sebastian Schelter s...@apache.org wrote:
I'm happy to anounce that a paper called Distributed Matrix
Factorization with MapReduce using a series of Broadcast-Joins written
by me
Same request here.
Can you share the paper?
On Tue, Jul 23, 2013 at 6:47 AM, 刘鎏 liuliu@gmail.com wrote:
Congratulations~
By the way, could the paper be shared? THX~
Best,
LiuLiu
On Mon, Jul 22, 2013 at 2:22 AM, Sebastian Schelter s...@apache.org
wrote:
I'm happy to anounce
Hi,
Consider this as a newbie question.
I have been reading about CF algorithms. Everyone seems to be taking the
preference value as ratings, or any singular attribute. However, in a
typical ecommerce scenario the entire clickstream data is important ( with
varying weights) to determine the
Congrats!! Could you share the link to the paper!
On Tue, Jul 23, 2013 at 6:24 PM, Koobas koo...@gmail.com wrote:
Same request here.
Can you share the paper?
On Tue, Jul 23, 2013 at 6:47 AM, 刘鎏 liuliu@gmail.com wrote:
Congratulations~
By the way, could the paper be shared? THX~
Here's just one perspective --
Yes this is kind of how things like ALS work. The input values are
viewed as 'weights', not ratings. They're not reconstructed directly
but used as a weight in a loss function. This turns out to make more
sense when paired with a squared-error loss function, as it
On Tue, Jul 23, 2013 at 6:07 AM, Jayesh jayesh.sidhw...@gmail.com wrote:
I have been reading about CF algorithms. Everyone seems to be taking the
preference value as ratings, or any singular attribute. However, in a
typical ecommerce scenario the entire clickstream data is important ( with
Thank you Sean, Ted.
I would dig more on the leads you gave and revert.
Ps: I love this community. It's so helpful!
On Tuesday, July 23, 2013, Ted Dunning wrote:
On Tue, Jul 23, 2013 at 6:07 AM, Jayesh
jayesh.sidhw...@gmail.comjavascript:;
wrote:
I have been reading about CF
Yes, the big problem for academia is the lack of access to real production
systems which would give much more diverse signals to learn from and the
possibility to evaluate on real users. Fortunately, this is slowly
beginning to change, Berlin-based company plista for examples offers a
recommender
This sounds great. Go for it. Put a comment on the design doc with a
pointer to text that I should import.
On Tue, Jul 23, 2013 at 9:39 AM, Pat Ferrel p...@occamsmachete.com wrote:
I can supply:
1) a Maven based project in a public github repo as a baseline that
creates the following
On Tue, Jul 23, 2013 at 9:39 AM, Pat Ferrel p...@occamsmachete.com wrote:
This pipeline lacks downsampling since I had to replace
PreparePreferenceMatrixJob and potentially LLR for [B'A]. I assume
Sebastian is the person to talk to about these bits?
I think that is a good source. If you
Will do.
For what it's worth…
The project I'm working on is an online recommender for video content. You go
to a site I'm creating, make some picks and get recommendations immediately
online. The training data comes from mining rotten tomatoes for critics
reviews. There are two actions,
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