That would be great:
Specifically if that is some kind of real usage data, and the results are
evaluated against a -without decay- baseline, via A/B tests measuring the
increase in conversion.
Best
Gokhan
On Wed, Nov 20, 2013 at 2:28 PM, Cassio Melo wrote:
> Hi guys, thanks for sharing your
Hi guys, thanks for sharing your experiences on this subject, really
appreciated. To summarize the discussion:
- The decay of old preference values might loose important historical data
in cases where the user has no recent activity (Gokhan)
- When using decay (or truncate preferences), the precis
> 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
On Fri, Nov 8, 2013 at 6:24 AM, Ted Dunning wrote:
> On Thu, Nov 7, 2013 at 12:50 AM, Gokhan Capan wrote:
>
> > This particular approach is discussed, and proven to increase the
> accuracy
> > in "Collaborative filtering with Temporal Dynamics" by Yehuda Koren. The
> > decay function is paramete
On Thu, Nov 7, 2013 at 12:50 AM, Gokhan Capan wrote:
> This particular approach is discussed, and proven to increase the accuracy
> in "Collaborative filtering with Temporal Dynamics" by Yehuda Koren. The
> decay function is parameterized per user, keeping track of how consistent
> the user behav
Not sure how you are going to decay in Mahout. Once ingested into Mahout there
are no timestamps. So you’ll have to do that before ingesting.
Last year we set up an ecom-department store type recommender with data from
online user purchase, add-to-cart, and view. The data was actual user behavio
Cassio,
I am not sure if there are direct/indirect ways to to this with existing
code.
Recall that an item neighborhood based score prediction, in simplest terms,
is a weighted average of the active user's ratings on other items, where
the weights are item-to-item similarities. Applying a decay f
Assuming that most recent ratings or implicit preference data is more
important than the older ones, I wonder if there is a way to decrease the
importance (score) of old preference entries without having to update all
previous preferences.
Currently I'm fetching new preferences from time to time a
