Hi, I'm delighted today
to have the opportunity to talk
to you about interdisciplinary perspectives on search.
Before I begin, I'd like to thank SIGCHI for
this Lifetime Research Award and the many colleagues
who I've worked with over the years at
Bell Labs and Microsoft and beyond,
without whom this award would not have been possible.
I've long been interested in how people
retrieve information from external sources.
Whether these sources be people,
books, or increasingly computer systems.
The success of
any information retrieval system depends critically
on both the accuracy
and efficiency of the underlying algorithms,
and more importantly, on the ability to support
people in articulating what they are
looking for and making sense of results.
In many ways, this talk could have been subtitled,
"If search doesn't work for people, it doesn't work."-
In the rest of my talk,
I want to illustrate this with three lines
of work that combined perspectives from
human-computer interaction and information retrieval
to design search systems.
The first of these is something
called Latent Semantic Analysis,
which I did very early on while I was at Bell Labs.
This effort was an attempt to remove
communication barriers between searchers and authors.
Stuff I've seen happened about 15 years later at
Microsoft and this work
enables people to not just find information,
but re-find information that they've seen before.
The final effort that I want to talk
about is work on personalized search.
Here the idea is to provide
more contextualized and
personalized experiences at web-scale.
My interest in search started after I finished
my PhD and joined the HCI group at Bell Labs.
This I believe was the first
Human-Computer Interaction group and industry.
Tom Landauer, who is shown here on the left,
was the leader of this group and he was
joined over the course of a few months by myself,
Luis Gomez, and George Furnas.
At that time, most search happened in the physical world.
People went to libraries,
they browsed the stacks,
and if they had a particular book in mind,
they might use the card catalog to find it.
People could search for phone numbers in
the white pages or businesses in the yellow pages.
Information was just starting to move online.
This is a screenshot of a Teletel system
that the phone companies were
starting to deploy around 1980.
It would be more than a decade after this,
before the first web-page was created in 1991,
and another several years before
the first web search engines started to gain popularity.
So my research started in
these very formative years as
information was moving from the
physical to digital world.
One of the first things that we discovered is what
we called the vocabulary mismatch problem.
When people seek information from external sources,
there's a kind of impedance mismatch
between the way that people who are looking for
that information describe what they're looking for and
the way that people who created
the information described it at the time.
Fundamentally, people don't always use the same language.
We saw this everywhere that we looked,
whether it was trying to find
information in the yellow pages,
trying to find command names for
operating systems like Unix.
What's ironic is that we actually call this a phenomenon,
different names over the years just
exacerbating the problem that we were trying to solve.
We called it Vocabulary mismatch,
verbal disagreement, or statistical semantics.
We described this work at the 0th CHI Conference.
It was a conference on Human Factors in
computer systems held in Maryland in March of 1982.
You can see images of the conference.
There are lots of men in suits and ties.
Judy Olson is giving her talk using
an overhead projector and you can see
the lovely early typesetting for our paper.
The paper that we presented here
was called Statistical semantics.
How can a computer use what people name
things to guess what things
people mean when they name things.
Now, admittedly, this is
a somewhat convoluted way of saying that there's
a mismatch between how authors describe
information and how people look for it,
and the job of a computer system
is to mitigate this mismatch.
We went on to note that
getting timely feedback was really,
in situ, as people were using the system,
would be really important to build great systems.
We said in the abstract,
system designers are at
a disadvantage in that they do not usually get explicit,
immediate, and continuous feedback from users.
Knowing this would help designers build systems
whose information is accessible
to the widest possible audience.
It's really interesting as I was looking back
on this to reflect how much this
foreshadowed the importance of
large-scale behavioral logs that are
really key parts of modern web services.
Nobody would design a Web Service today without
the ability to understand how people
are using it and more importantly,
to improve the system with this knowledge.
Latent semantic analysis is perhaps
the best-known solution to
the vocabulary mismatch problem that I just described.
It's based on very simple principle that words which
occur in similar contexts tend to have similar meaning.
Take for example the words physician and doctor,
they may never co-occur in a document,
yet they keep very similar company.
They both occur with words like nurse,
patient, hospital, treatment, recovery, and so on.
Using a mathematical technique
called singular value decomposition,
we identify patterns of word usage like this
and used a lower-dimensional representation
to summarize these patterns.
For those of you who know factor analysis,
it's a very similar idea.
Latent semantic analysis has
been used for many applications.
When applied to information retrieval,
it allows searchers to find
documents that contain the word physician,
even when they search for doctor.
Jakob Nielsen and I used it to assign
reviewers to submitted documents in CHI 1993.
Tom Landauer and I used it to take
the Test of English as a foreign language,
and LSA did about as well as the average test taker.
Tom and I also used it to model
vocabulary acquisition in children.
The key to all of these and
other applications is to learn
a representation that mitigates
the vocabulary mismatch between searchers and authors.
As the name Stuff I've Seen suggests,
search is not just about discovering new information,
but also often involves
refinding information that you've seen before.
In order to help support people in re-finding,
we use two principles:
the first was to unify
access across different information silos.
We wanted to make it easy for people to find e-mail,
files, Web without having to
worry about where they saw that information.
We also wanted to support people
in specifying what they
remembered about this information.
When you've seen information before,
you often remember when you saw it,
who created it, what type of file it was, and so on.
The first system we developed to
support this was called Stuff I've Seen,
and it is a very rich
attribute-based view of information.
It allows people to quickly slice
and dice and pivot based on what they
remember about a particular target and do
so in a way that allows them to hone in very efficiently.
Time it turns out is
a very important attribute and
so we've developed further enhancements
called memory landmarks that provide
time-based anchors of salient events
from your own photos,
files, or significant news
events that are happening in the world in
order to help you get to the right episodic information.
This work highlights importantly that we need to support
systems that enable people to discover new information,
as well as re-finding
information that you've seen before.
The final example that I want to touch on
briefly is work that
I've done over the last decade or so,
on context and personalization in web search.
It turns out that queries are exceedingly
difficult to interpret in isolation.
The query that I've shown here,
SIGIR, occurs in more than three million web-pages.
This task becomes much easier if we can begin to
model who is asking the query,
what they've done in the past,
where they are, when it is,
what events are happening in the world on that,
when the query is issued, and so on.
So if I issue the query, SIGIR,
I typically mean ACM Special Interest Group
in information retrieval,
whose web page is shown below.
If Stuart Bowen Jr,
issues the query, on the other hand,
Stuart was the Special Inspector General
for Iraq Reconstruction.
He probably means
the government site on Iraq reconstruction.
There are other contexts like this that can
be quite useful in interpreting a query.
It might be the immediately
preceding search in a session.
It might be location in some cases,
it might be time of year or event in others.
The bottom line here is that
using a single ranking for every person,
in every context, at every point in
time fundamentally limits how
well a search engine can do.
Whenever the same query
means different things to different people,
you will not be able by definition to
satisfy them all without some kind of personalization.
I want to wrap up by talking
a little bit about what's next in the world of search.
We see increasingly people issuing search on
the go in spoken queries rather than typed queries.
More and more systems are
anticipating what people might need and
proactively finding that information
rather than waiting for an explicit search query.
I think we're also seeing
a very interesting convergence of
digital and physical information
as objects in the physical world
become more and more connected.
I think that we in the CHI community
have tremendous opportunity
here to shape how such systems designed and developed.
In just a few decades,
search has gone from being a rather arcane skill,
to something that is a core fabric
of people's everyday lives.
Today we all use search,
multiple times a day to find all kinds of information,
to buy things, to monitor breaking news and events,
to understand the merging medical conditions and so on.
Because of this ubiquity of search,
I believe it's more important now than ever to
understand and support people in creating,
finding, and making sense of information.
The CHI community should lead the way
in shaping these information futures,
using the broad range of
interdisciplinary perspectives that will
be required to do so.
I want to give one last shout-out to
my many collaborators over the years.
Thank you-all for the inspiration
and the insights that you've provided for my work,
which makes this award talk possible.
Thank you-all again.
I believe I now have time for a few questions.