RE: Temporal effects. In CF you are interested in similarities. For instance in
a User-based CF recommender you want to detect users similar to a given user.
The time decay of the similarities is likely to be very slow. In other word if
I bought an iPad 1 and you bought an iPad 1, the
Pat,
This is an important effect and it strongly informs how you should
down-sample heavy users as well as how you should handle temporal dynamics.
On Sat, Feb 2, 2013 at 9:54 AM, Pat Ferrel pat.fer...@gmail.com wrote:
RE: Temporal effects. In CF you are interested in similarities. For
Indeed, please elaborate. Not sure what you mean by this is an important
effect
Do you disagree with what I said re temporal decay?
As to downsampling or rather reweighting outliers in popular items and/or
active users--It's another interesting question. Does the fact that we both
like
On Sat, Feb 2, 2013 at 1:03 PM, Pat Ferrel pat.fer...@gmail.com wrote:
Indeed, please elaborate. Not sure what you mean by this is an important
effect
Do you disagree with what I said re temporal decay?
No. I agree with it. Human relatedness decays much more quickly than item
popularity.
Hi Guys,
I'm rather new to the whole Mahout ecosystem, so please excuse if the questions
I have are rather dumb ;)
Our problem basically boils down to this: we want to match users with either
the content they interested in and/or the content they could contribute to. To
do this matching we
It's a good question. I think you can achieve a partial solution in Mahout.
Real-time suggests that you won't be able to make use of
Hadoop-based implementations, since they are by nature big batch
processes.
All of the implementations accept the same input -- user,item,value.
That's OK; you can
