Thank you Sean, Ted. I would dig more on the leads you gave and revert. Ps: I love this community. It's so helpful!
On Tuesday, July 23, 2013, Ted Dunning wrote: > On Tue, Jul 23, 2013 at 6:07 AM, Jayesh > <[email protected]<javascript:;>> > wrote: > > > > > > > I have been reading about CF algorithms. Everyone seems to be taking the > > preference value as ratings, or any singular attribute. However, in a > > typical ecommerce scenario the entire clickstream data is important ( > with > > varying weights) to determine the affinity of the user vs item. > > > > Yes. This is the literature, but it is the opposite in practice. Ratings > rarely convey as much information as the much richer and more voluminous > stream of implicit data. > > Even worse, almost all academic research ignores the fact that multiple > kinds of behavior is involved in a real system. > > Check out my talk at Buzzwords for a possible solution for you. > > > > If we consider many parameters, do we use any kind of a regression to > > formulate the affinity score (that takes into consideration all the > > features and their respective weights that impact the users liklehood) > and > > run any CF algorithm over these scores? > > > > Bayesian bandit is what I would recommend. > -- Sent from mobile device
