mahout-user mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: Number of features for ALS
Date Mon, 07 Apr 2014 12:52:57 GMT
That book is a fine beginning, but doesn't have a lot of detail.

Check out Pat's very nice demo site for more information.  I have also
given a ton of talks on the subject.

And, to answer your question, cooccurrence recommendation works great with
diverse sources of behavior.



On Sun, Apr 6, 2014 at 8:40 PM, Niklas Ekvall <niklas.ekvall@gmail.com>wrote:

> Thanks Pat!
>
> I did find a book by Ted Dunning and Ellen Friedman (Practical Machine
> Learning: Innovations in Recommendations) I guess I can us it to read more
> about co-occurrence recommender or co-occurrence analysis.
>
> Best, Niklas
>
>
>
> 2014-04-06 19:37 GMT+02:00 Pat Ferrel <pat@occamsmachete.com>:
>
> > >
> > > On Apr 6, 2014, at 2:48 AM, Niklas Ekvall <niklas.ekvall@gmail.com>
> > wrote:
> > >
> > > Hi Pat and Ted!
> > >
> > > Yes I agree with about the rank and MAP. But in this case, that is a
> good
> > > initial guess on the parameters *number of features* and *lambda*?
> >
> > 20 or 30 features depending on the variance in your data, more is
> > theoretically better but usually give rapidly diminishing returns. I
> forget
> > what lambdas we tried
> >
> > >
> > > Where can I find the best article about cooccurrence recommender? And
> can
> > > one use this approach for different types of data, e.g., ratings,
> > purchase
> > > histories or click histories?
> >
> > Absolutely, but remember that the data you train on is what you are
> > recommending. So if you train on detail-views (click paths) the
> recommender
> > will return items to look at, not necessarily the same as items to
> > purchase. If you train on what you want to recommend then all of the
> above
> > will work.
> >
> > If you want to train on click-paths and recommend purchase you probably
> > need a cross-recommender another discussion altogether.
> >
> > >
> > > Best, Niklas
> > >
> > >
> > > 2014-03-31 7:53 GMT+02:00 Ted Dunning <ted.dunning@gmail.com>:
> > >
> > >> Yeah... what Pat said.
> > >>
> > >> Off-line evaluations are difficult.  At most, they provide directional
> > >> guidance to be refined using live A/B testing.  Of course, A/B testing
> > of
> > >> recommenders comes with a new set of tricky issues like different
> > >> recommenders learning from each other.
> > >>
> > >> On Sun, Mar 30, 2014 at 4:54 PM, Pat Ferrel <pat@occamsmachete.com>
> > wrote:
> > >>
> > >>> Seems like most people agree that ranking is more important than
> rating
> > >> in
> > >>> most recommender deployments. RMSE was used for a long time with
> > >>> cross-validation (partly because it was the choice of Netflix during
> > the
> > >>> competition) but it is really a measure of total rating error.  In
> the
> > >> past
> > >>> we've used mean-average-precision as a good measure of ranking
> quality.
> > >> We
> > >>> chose hold-out tests based on time, so something like 10% of the most
> > >>> recent data was held out for cross-validaton and we measured MAP@nfor
> > >>> tuning parameters.
> > >>>
> > >>>
> > >>
> >
> http://en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision
> > >>>
> > >>> For our data (ecommerce shopping data) most of the ALS tuning
> > parameters
> > >>> had very little affect on MAP. However cooccurrence recommenders
> > >> performed
> > >>> much better using the same data. Unfortunately comparing two
> algorithms
> > >>> with offline tests is of questionable value. Still with nothing else
> to
> > >> go
> > >>> on we went with the cooccurrence recommender.
> > >>>
> > >>>
> > >>
> > >
> >
>

Mime
  • Unnamed multipart/alternative (inline, None, 0 bytes)
View raw message