On Tue, Jul 23, 2013 at 6:07 AM, Jayesh <jayesh.sidhw...@gmail.com> 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.
