[NOISE].
There are many more advanced learning
algorithms than the regression based
reproaches.
And they generally
account to theoretically
optimize or retrieval method.
Like map or nDCG.
Note that the optimization objecting
function that we have seen
on the previous slide is not directly
related to retrieval measure.
Right?
By maximizing the prediction of one or
zero.
Or we don't necessarily optimize
the ranking of those documents.
One can imagine that why,
our prediction may not be too bad and
let's say both are around 0.5.
So it's kind of in the middle of zero and
one for
the two documents, but
the ranking can be wrong.
So we might have the, a larger value for.
D2 and then e1.
So that won't be good from
retrieval perspective,
even though by likelihood function,
it's not bad.
In contrast, we might have another
case where we predicted values.
Or around 0.9 let's say,
and by the objective function,
the error will be larger, but if we
can get the order of the two documents
correct, that's actually a better result.
So these new more advanced approaches
will try to correct that problem.
Of course then the challenge is that.
That the optimization problem
will be harder to solve.
And then researchers have proposed
many solutions to the problem.
And you can read more of
the references at the end.
Know more about the these approaches.
Now these learning to random approaches.
Are actually general, so they can also be
applied to many other ranking problems,
not just retrieval problem.
So here I list some for
example recommender systems,
computational adv, advertising,
or summarization, and
there are many others that you can
probably encounter in your applications.
To summarize this lecture,
we have talked about, using machine
learning to combine much more features
to incorporate a ranking without.
Actually the use of machine learning,
in information retrieval has
started since many decades ago.
So for example on the Rocchio feedback
approach that we talked about earlier
was a machine learning approach
applied to to learn this feedback, but
the most reasonable use of machine
learning has been driven by some changes.
In the environment of applications
of retrieval systems.
And first it's, mostly,
driven by the availability of a lot of
training data in the form of clicks rules.
Such data weren't available before.
So the data can provide a lot
of useful knowledge about
relevance and machine learning methods
can be applied to leverage this.
Secondly it's also due by
the need of combining them.
In the features.
And
this is not only just because there
are more features available on the web
that can be naturally re-used
with improved scoring.
It's also because by combining them,
we can improve the robustness of ranking.
So this is designed for combating spams.
Modern search engines all use some kind
of machine learning techniques to combine
many features to optimize ranking and
this is a major feature of these
current engines such as Google, Bing.
The topic of learning to rank
is still active research.
Topic in the community, and so you can
expect to see new results being developed,
in the next, few years.
Perhaps.
Here are some additional readings that
can give you more information about.
About, how learning to rank books and
also some advanced methods.
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