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This lecture is about
the future of web search.
In this lecture, we're going to talk
about some possible future trends
of web search and intelligent information
retrieval systems in general.
In order to further improve
the accuracy of a search engine,
it's important that to consider
special cases of information need.
So one particular trend could be to
have more and more specialized than
customized search engines, and they
can be called vertical search engines.
These vertical search engines can be
expected to be more effective than
the current general search engines
because they could assume that
users are a special group of users that
might have a common information need,
and then the search engine can be
customized with this ser, so, such users.
And because of the customization,
it's also possible to do personalization.
So the search can be personalized,
because we have a better
understanding of the users.
Because of the restrictions with domain,
we also have some advantages
in handling the documents, because we can
have better understanding of documents.
For example, particular words may
not be ambiguous in such a domain.
So we can bypass the problem of ambiguity.
Another trend we can expect to see,
is the search engine will
be able to learn over time.
It's like a lifetime learning or
lifelong learning, and this is, of course,
very attractive because that means the
search engine will self-improve itself.
As more people are using it, the search
engine will become better and better, and
this is already happening,
because the search engines can learn
from the [INAUDIBLE] of feedback.
More users use it, and the quality
of the search engine allows for
the popular queries that are typed in by
many users allow it to become better,
so this is sort of another
feature that we will see.
The third trend might be
to the integration of
bottles of information access.
So search, navigation, and
recommendation or filtering might be
combined to form a full-fledged
information management system.
And in the beginning of this course,
we talked about push versus pull.
These are different modes of information
access, but these modes can be combined.
And similarly, in the pull mode, querying
and the browsing could also be combined.
And in fact we're doing that basically,
today, is the [INAUDIBLE] search endings.
We are querying, sometimes browsing,
clicking on links.
Sometimes we've got some
information recommended.
Although most of the cases the information
recommended is because of advertising.
But in the future, you can imagine
seamlessly integrate the system with
multi-mode for information access, and
that would be convenient for people.
Another trend is that we might see systems
that try to go beyond the searches
to support the user tasks.
After all, the reason why people want
to search is to solve a problem or
to make a decision or perform a task.
For example consumers might search for
opinions about products in
order to purchase a product,
choose a good product by, so
in this case it would be beneficial to
support the whole workflow of purchasing
a product, or choosing a product.
In this era, after the common search
engines already provide a good support.
For example, you can sometimes look at the
reviews, and then if you want to buy it,
you can just click on the button to go the
shopping site and directly get it done.
But it does not provide a,
a good task support for many other tasks.
For example, for researchers,
you might want to find the realm in
the literature or site of the literature.
And then, there's no, not much support for
finishing a task such as writing a paper.
So, in general, I think,
there are many opportunities in the wait.
So in the following few slides, I'll
be talking a little bit more about some
specific ideas or thoughts that hopefully,
can help you in imagining new
application possibilities.
Some of them might be already relevant
to what you are currently working on.
In general, we can think about any
intelligent system, especially intelligent
information system, as we specified
by these these three nodes.
And so
if we connect these three into a triangle,
then we'll able to specify
an information system.
And I call this
Data-User-Service Triangle.
So basically the three questions you
ask would be who are you serving and
what kind of data are you are managing and
what kind of service you provide.
Right there, this would help us
basically specify in your system.
And there are many different ways
to connect them depending on
how you connect them,
you will have a different kind of systems.
So let me give you some examples.
On the top,
you can see different kinds of users.
On the left side, you can see different
types of data or information, and
on the bottom,
you can see different service functions.
Now imagine you can connect
all these in different ways.
So, for example, you can connect
everyone with web pages, and
the support search and
browsing, what do you get?
Well, that's web search, right?
What if we connect UIUC employees with
organization documents or enterprise
documents to support the search and
browsing, but that's enterprise search.
If you connect the scientist
with literature information
to provide all kinds of service,
including search, browsing, or
alert of new random documents or
mining analyzing research trends,
or provide the task with support or
decision support.
For example, we might be,
might be able to provide a support for
automatically generating
related work section for
a research paper, and
this would be closer to task support.
Right?
So then
we can imagine this would
be a literature assistant.
If we connect the online shoppers
with blog articles or product reviews
then we can help these people
to improve shopping experience.
So we can provide, for example data mining
capabilities to analyze the reviews,
to compare products, compare sentiment of
products and to provide task support or
decision support to have them
choose what product to buy.
Or we can connect customer service
people with emails from the customers,
and, and we can imagine a system
that can provide a analysis
of these emails to find that the major
complaints of the customers.
We can imagine a system we
could provide task support
by automatically generating
a response to a customer email.
Maybe intelligently attach
also a promotion message
if appropriate, if they detect that that's
a positive message, not a complaint, and
then you might take this opportunity
to attach some promotion information.
Whereas if it's a complaint,
then you might be able to
automatically generate some
generic response first and
tell the customer that he or she can
expect a detailed response later, etc.
All of these are trying to help
people to improve the productivity.
So this shows that
the opportunities are really a lot.
It's just only restricted
by our imagination.
So this picture shows the trend
of the technology, and also,
it characterizes the, intelligent
information system in three angles.
You can see in the center, there's
a triangle that connects keyword queries
to search a bag of words representation.
That means the current search engines
basically provides search support
to users and mostly model
users based on keyword queries
and sees the data through
bag of words representation.
So it's a very simple approximation of
the actual information in the documents.
But that's what the current system does.
It connects these three nodes
in such a simple way, or
it only provides a basic search function
and doesn't really understand the user,
and it doesn't really understand that
much information in the documents.
Now, I showed some trends to push each
node toward a more advanced function.
So think about the user node here, right?
So we can go beyond the keyword queries,
look at the user search history,
and then further model the user
completely to understand the,
the user's task environment,
task need context or other information.
Okay, so this is pushing for
personalization and complete user model.
And this is a major
direction in research in,
in order to build intelligent
information systems.
On the document side,
we can also see, we can
go beyond bag of words implementation
to have entity relation representation.
This means we'll recognize people's names,
their relations, locations, etc.
And this is already feasible with
today's natural processing technique.
And Google is the reason
the initiative on the knowledge graph.
If you haven't heard of it,
it is a good step toward this direction.
And once we can get to that level without
initiating robust manner at larger scale,
it can enable the search engine
to provide a much better service.
In the future we would like to have
knowledge representation where we
can add perhaps inference rules, and
then the search engine would
become more intelligent.
So this calls for
large-scale semantic analysis, and
perhaps this is more feasible for
vertical search engines.
It's easier to make progress
in the particular domain.
Now on the service side,
we see we need to go beyond the search of
support information access in general.
So search is only one way to get access
to information as well recommender
systems and push and pull so different
ways to get access to random information.
But going beyond access,
we also need to help people digest the
information once the information is found,
and this step has to do with analysis
of information or data mining.
We have to find patterns or
convert the text information into
real knowledge that can
be used in application or
actionable knowledge that can be used for
decision making.
And furthermore the knowledge
will be used to help a user to
improve productivity in finishing a task,
for example, a decision-making task.
Right, so this is a trend.
And, and, and so basically,
in this dimension, we anticipate
in the future intelligent information
systems will provide intelligent and
interactive task support.
Now I should also emphasize interactive
here, because it's important to optimize
the combined intelligence of the users and
the system.
So we, we can get some help
from users in some natural way.
And we don't have to assume the system
has to do everything when the human,
user, and the machine can collaborate in
an intelligent way, an efficient way,
then the combined intelligence
will be high and in general,
we can minimize the user's overall
effort in solving problem.
So this is the big picture of future
intelligent information systems,
and this hopefully can provide
us with some insights about
how to make further innovations
on top of what we handled today.
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