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 clear that there is a lot of gain to be had there. If you want to see high gain, look at how the recommendation system feeds itself and how alternative exploration options feed the recommendation system. 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 data. Not exploring now pays off today because you recommend what you know already works. How to trade these off is a big question. Presumably the things one learns from Bayesian Bandits would be very helpful in this respect. > 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 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 > of > > the practical benefit to modeling the time dynamics goes away since you > > aren't predicting ratings in any case. > > > > >
