Ok, I'll have a look. Thanks! I know mahout is intended for large scale
machine learning, but I guess it shouldn't have problems with such small data
either.
Ted Dunning ted.dunn...@gmail.com schrieb:
On Thu, Nov 7, 2013 at 9:45 PM, Andreas Bauer b...@gmx.net wrote:
Hi,
Thanks for your
I think the intuition here is, when making an item neighborhood base
recommendation, to penalize the contribution of the items that the user has
rated a long time ago. I didn't test this in a production recommender
system, but I believe this might result in recommendation lists with better
You are correct that it should work with smaller data as well, but the
trade-offs are going to be very different.
In particular, some algorithms are completely infeasible at large scale,
but are very effective at small scale. Some like those used in glmnet
inherently require multiple passes
Pat,
Would all of this be on Solr/Lucene 4.5.1?
Mahout's presently at Lucene 4.3.1 (as of 0.8) , but we should be moving to the
latest stable release at time of 0.9 release.
On Thursday, November 7, 2013 10:33 PM, Pat Ferrel pat.fer...@gmail.com wrote:
Another approach would be to
For recommendation work, I suggest that it would be better to simply code
out an explicit OR query.
On Thu, Nov 7, 2013 at 8:11 PM, Ken Krugler kkrugler_li...@transpac.comwrote:
Hi Pat,
On Nov 7, 2013, at 7:30pm, Pat Ferrel pat.fer...@gmail.com wrote:
Another approach would be to weight
One other thing I should have mentioned is that if you care about setting
weights on incoming terms, you can boost them using the ^value syntax.
E.g. the_kings_speech^1.5 OR skyfalll^0.5 OR looper^3.0…
If you want to account for weights of terms in the index, it's a bit harder.
You can do
not including Solr in the project so it should work with any recent version.
I’m actually using 4.2
On Nov 8, 2013, at 9:24 AM, Suneel Marthi suneel_mar...@yahoo.com wrote:
Pat,
Would all of this be on Solr/Lucene 4.5.1?
Mahout's presently at Lucene 4.3.1 (as of 0.8) , but we should be moving
Not planning to do anything with weights at present. An ORed query should
suffice for the time being and Solr weights. There are a good list of ways to
do this later if it warrants an experiment. Thanks.
Have, similar items as input, recommendations from user “likes”, and just got
recs from
