Thanks, I will take a look at the Implicit Feedback literature to see how
it can apply to my situation. Are you aware of any time aware implicit
feedback models?
On Tue, Apr 30, 2013 at 11:46 AM, Ted Dunning ted.dunn...@gmail.com wrote:
Keep in mind that time dynamics generally have benefit
On Wed, May 1, 2013 at 9:28 AM, Chirag Lakhani clakh...@zaloni.com wrote:
Thanks, I will take a look at the Implicit Feedback literature to see how
it can apply to my situation. Are you aware of any time aware implicit
feedback models?
No. I don't know of any work on that. I am also not
On 5/1/13 11:03 AM, Ted Dunning ted.dunn...@gmail.com wrote:
The question here is how to get the recommendation engine to explore more
when appropriate and less when no. Exploring now (i.e. bringing in more
diversity in recommendations) pays off tomorrow because the system gets
new
kinds of
I was wondering if the collaborative filtering library in Mahout has any
algorithms that incorporate concept drift i.e. time dynamics. From my own
research I have come across the BellKor algorithm called TimeSVD++ and
there is a recent paper using hidden markov models with collaborative
No, time is in the data model but nothing uses it that I know of.
On Tue, Apr 30, 2013 at 3:18 PM, Chirag Lakhani clakh...@zaloni.com wrote:
I was wondering if the collaborative filtering library in Mahout has any
algorithms that incorporate concept drift i.e. time dynamics. From my own
Do you know of any other large scale machine learning platforms that do
incorporate it?
On Tue, Apr 30, 2013 at 10:21 AM, Sean Owen sro...@gmail.com wrote:
No, time is in the data model but nothing uses it that I know of.
On Tue, Apr 30, 2013 at 3:18 PM, Chirag Lakhani clakh...@zaloni.com
GraphLab -- http://docs.graphlab.org/collaborative_filtering.html#SVD_PLUS_PLUS
On Tue, Apr 30, 2013 at 3:30 PM, Chirag Lakhani clakh...@zaloni.com wrote:
Do you know of any other large scale machine learning platforms that do
incorporate it?
On Tue, Apr 30, 2013 at 10:21 AM, Sean Owen
Keep in mind that time dynamics generally have benefit for predicting
ratings. The point is that the average rating for a person goes up and
down over time even if their general taste doesn't change. Likewise for an
item.
If you use implicit feedback and recommend based on recent behavior most
