Use k-fold cross-validation or hold-out tests for estimating the quality
of different parameter combinations.
--sebastian
On 03/30/2014 11:53 AM, Niklas Ekvall wrote:
Hi,
My name is Niklas Ekvall and I have a implementation of the recommender
algorithm "Large-scale Parallel Collaborative Filtering for the Netflix
Prize" and now I'm wondering how to choose the number of features and
lambda. Could any of guys help me to explain a stepwise strategy to choose
or optimize these two parameters?
Best regards, Niklas
2014-03-27 19:07 GMT+01:00 j.barrett Strausser <
j.barrett.straus...@gmail.com>:
Thanks Ted,
Yes for the time problem. We tend to use aggregations of session data. So
instead of asking for user recommendations we do things like user+sessions
recommendations.
Of course, deciding when sessions start and stop isn't trivial. I ideally
what I would want to is time-weight views using a kernel or convolution.
That's a bit heavy so we typically have a global model, that is is
basically all preferences over times. Then these user+session type models.
We can then combine these at another level to give recommendations based on
what you like throughout time versus what you have been doing recently.
-b
On Thu, Mar 27, 2014 at 1:59 PM, Ted Dunning <ted.dunn...@gmail.com>
wrote:
For the poly-syllable challenged,
hetereoscedasticity - degree of variation changes. This is common with
counts because you expect the standard deviation of count data to be
proportional to sqrt(n).
time imhogeneity - changes in behavior over time. One way to handle this
(roughly) is to first remove variation in personal and item means over
time
(if using ratings) and then to segment user histories into episodes. By
including both short and long episodes you get some repair for changes in
personal preference. A great example of how this works/breaks is
Christmas
music. On December 26th, you want to *stop* recommending this music so
it
really pays to limit histories at this point. By having an episodic user
session that starts around November and runs to Christmas, you can get
good
recommendations for seasonal songs and not pollute the rest of the
universe.
On Thu, Mar 27, 2014 at 8:30 AM, j.barrett Strausser <
j.barrett.straus...@gmail.com> wrote:
For my team it has usually been hetereoscedasticity and time
inhomogeneity.
On Thu, Mar 27, 2014 at 10:18 AM, Tevfik Aytekin
<tevfik.ayte...@gmail.com>wrote:
Interesting topic,
Ted, can you give examples of those mathematical assumptions
under-pinning ALS which are violated by the real world?
On Thu, Mar 27, 2014 at 3:43 PM, Ted Dunning <ted.dunn...@gmail.com>
wrote:
How can there be any other practical method? Essentially all of
the
mathematical assumptions under-pinning ALS are violated by the real
world.
Why would any mathematical consideration of the number of features
be
much
more than heuristic?
That said, you can make an information content argument. You can
also
make
the argument that if you take too many features, it doesn't much
hurt
so
you should always take as many as you can compute.
On Thu, Mar 27, 2014 at 6:33 AM, Sebastian Schelter <
s...@apache.org>
wrote:
Hi,
does anyone know of a principled approach of choosing the number
of
features for ALS (other than cross-validation?)
--sebastian
--
https://github.com/bearrito
@deepbearrito
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https://github.com/bearrito
@deepbearrito
