some sort of lung cancer, I don't want to send you to an ophthalmologist
who's really good at curing high diseases, but useless when it comes to a lung.
So somewhere in here, we're going to have to have some notion of search or filter.
Because if we don't think about this as I can rank the oncologist for
you, but I'm certainly not going to go back there, and
just say well let's find a doctor you would like.
And what do I end up with, I end up with a foot doctor who has a really good sense of
humor and no ability to do the surgery that you want.
So we're going to need to have a pretty strongly taxonomized set of items.
And for that reason our recommendations may only be from 10 or
20 maybe 50 people in a particular specialty,
from among all the specialty that we have.
What's jumps out to you?
>> That's the big thing.
We've got this big constraint problem, because we need to know, not only what is
the person that I interact with, what doctor's going to be a good fit for them.
Collaborative filtering might be able to help us assess fit, but we're going to
need this constraint to see if the physician matches what the person needs.
Now, for a body of work to be useful for solving that particular problem.
What comes to mind is the group of Hector Garcia-Molina's group at Stanford,
on doing course recommendation.
Where they're trying to figure out, what courses can we recommend a student,
but keeping in mind, prerequisite structures and things like that,
that severely constrain the space of valid items at any given point?
So if I'm working on this problem, I would start by going and
reading that literature to see how they solved some of these issues, and
see if some of the techniques are going to translate over here.
But yeah, the constraint is also the big thing that jumps out at me.
But also there's opportunity for
even a little bit more data that we haven't talked about yet.
Which is particularly for cases that are going to need
follow up maybe with physical therapist is adherence levels.