With such small data, this sounds (without thinking too much) like you are
doing reasonably well with LLR similarity.
Have you tried a factorizing recommender?
On Sun, Jul 14, 2013 at 10:49 PM, Jayesh jayesh.sidhw...@gmail.com wrote:
Hi Ted,
Thanks for the reply.
My training data could
Is a factorizing recommender a better idea for low volume data in general?
On Mon, Jul 15, 2013 at 11:35 AM, Ted Dunning ted.dunn...@gmail.com wrote:
With such small data, this sounds (without thinking too much) like you are
doing reasonably well with LLR similarity.
Have you tried a
I think so, but I cannot say that I know so.
On Mon, Jul 15, 2013 at 8:37 AM, Koobas koo...@gmail.com wrote:
Is a factorizing recommender a better idea for low volume data in general?
On Mon, Jul 15, 2013 at 11:35 AM, Ted Dunning ted.dunn...@gmail.com
wrote:
With such small data, this
Okay. I'll try that and get back with the results.
Thank You
On Monday, July 15, 2013, Ted Dunning wrote:
I think so, but I cannot say that I know so.
On Mon, Jul 15, 2013 at 8:37 AM, Koobas koo...@gmail.com javascript:;
wrote:
Is a factorizing recommender a better idea for low volume
Hello,
I am exploring the collaborative filtering algorithms in Mahout to build a
recommendation engine.
I had recently gone for a Big Data conference where the speakers suggested
that using Mahout is overkill for anything that doesn't have some terabytes
of training data.
I tried to google
Mahout will work fine for smaller data sizes.
Collaborative filtering can be difficult in general with small data,
however.
How many users and how many items? How many actions?
On Sun, Jul 14, 2013 at 10:22 PM, Jayesh jayesh.sidhw...@gmail.com wrote:
Hello,
I am exploring the
Hi Ted,
Thanks for the reply.
My training data could have around 100k users and around 1k items. The data
is sparse (I have a boolean affinity - the user either bought the item or
did not)
PS: I have been playing around with a sample code, using Loglikelihood
Similarity to get a 24% precision,
