Welcome back. In this pair of lectures, we're going to
look at Recommender Systems as a whole and work through a taxonomy of
those systems looking at different dimensions for analyzing and
classifying them. And then specificially in the second part
of this pair, we're going to look at the
algorithims inside, at a overview level. We're calling this lecture 1-4 a and b,
because they are meant to work together, but we have
put them into two parts for those of you who don't
want to sit through Quite so long a lecture in
one chunk. So, the learning objectives are to understand different types of recommender
systems, including learning a framework for
analyzing recommender systems in general, and the specific algorithms And,
as we do that to build a road map for the rest of the course, which will be organized
around those algorithms.