the perfect search engine is not enough jaime teevan †, christine alvarado †, mark s. ackerman...

The Perfect Search Engine Is Not Enough

Jaime Teevan†, Christine Alvarado†, Mark S. Ackerman‡ and David R. Karger†

† MIT, CSAIL‡ University of Michigan

Let Me Interview You!

Email:–What’s the last email you read? What did you do with it? –Have you gone back to an email you’ve read before?

Web:

Files:

–What’s the last Web page you visited? How did you get there?–Have you looked for anything on the Web?

–What’s the last file you looked at? How did you get to it?–Have you looked for a file?

SearchOverview:Understanding

Introduction Related work Methodology What we learned

– How?– Why?– Who?– So what?

Directed

Introduction Related work Methodology What we learned

– How?– Why?– Who?– So what?

Haystack:Personal Information Storage

Email Web pages

Files Calendar

Contacts

Haystack

Directed Search in Haystack

What was that paper I read last week about

Information Retrieval?Haystack

Directed Search in Haystack

Ah yes! Thank you.

Haystack

“Perfect Search Engine”

Related Work

Directed search– Lab studies [Capra03, Maglio97]– Log analysis [Broder02, Spink01]

Observational studies [Malone83]

Information Seeking– Marchionini, O’Day and Jeffries, Bates, Belkin, …– Evolving information need

Modified Diary Study

Subjects: 15 CS graduate students Ten interviews each (2/day x 5 days) Two question types

– Last email/file/Web page looked at– Last email/file/Web page looked for

Supplemented with direct observation and an hour-long semi-structured interview

Directed SearchOverview:Understanding

Introduction Related work Methodology What we learned

– How?– Why?– Who?– So what?

Directed Search Today

Target: Connie Monroe’s office number

Type into a search engine: “Connie Monroe, office number”

What We Observed

Interviewer: Have you looked for anything on the Web today?Jim: I had to look for the office number of the Harvard professor.I: So how did you go about doing that?J: I went to the homepage of the Math department at Harvard

What We Observed

I: So you went to the Math department, and then what did you do over there?J: It had a place where you can find people and I went to that page and they had a dropdown list of visiting faculty, and so I went to that link and I looked for her name and there it was.

What We Observed

J: I knew that she had a very small Web page saying, “I’m here at Harvard. Here’s my contact information.”

Strategies Looking for Information

Teleporting

Orienteering

Why Do People Orienteer?

Easier than saying what you want You know where you are You know what you find

The tools don’t work

Easier Than Saying What You Want

Describing the target is hard– Can’t– Prefer not to

Habit – “Whichever way I remember first.”

Search for source– E.g., Your last email search

You Know Where You Are

Stay in known space– URL manipulation– Bookmarks– History

Backtracking– Following an information scent– Never end up at a dead end

You Know What You Find

Context gives understanding of answer

“I was looking for a specific file. But even when I saw its name, I wouldn’t have known that that was the file I wanted until I saw all of the other names in the same directory…”

Understanding negative results

“I basically clicked on every single button until I was convinced… I don’t think that it exists…”

Individual Search Behavior

Search behavior varied by individual Categorize based on email usage

– Filers– Pilers

People who pile information take small steps People who file information take big steps

0 1 2 3 4 5 6 7 8 9

ABCDEFGHIJKLM

Keyword Search Other

How Individuals Search For Files

Filers

Pilers

Big steps

Small steps

More to Learn from the Data

Differences in finding v. re-finding How organization relates to search Importance of type (email, files and Web) Looked at v. looked for

Keep in mind population

Support orienteering

Applying What We Learned

Advantages to orienteering– Easier than saying what you want– You know where you are– You know what you find

Individual differences in step size

– Highlight source (e.g., flag sources with info) – Integrate tools used for steps– Support exhaustive search

– Allow for different step sizes

More to Learn from the Data

Differences in finding v. re-finding How organization relates to search Importance of type (email, files and Web) Looked at v. looked for

Keep in mind population

Structural Consistency Important

All must be the same to re-find the information!

Supports orienteering for re-finding Allows access to new information

Preserve What User Remembers

File or Pile Email

0

1

2

3

4

5

6

7

8

0 20 40 60 80 100

% found in Inbox

# o

f se

arch

es

Filer

Piler

Searching Other Collections

Ah yes! Thank you.

Keep Population in Mind

CS grad students not representative Very familiar with search tools

Would expect to see lots of tool use

Orienteer to specific information

Relating How and What

People only keyword search 39% of the time What people look for related to how they look

Specific General Document

Other 47 19 41

Keyword 34 23 17

Surprise:

Relating How and Corpus

Email and files: Almost never keyword searched Easy to associate information with document Web: Used keyword search much more often

Email Files Web

Other 59 42 19

Keyword 06 10 64

Relating What and Corpus

Email Files Web

Specific 39 7 33

General 10 7 30

Document 08 35 14

Email searches were primarily for specific information File searches were primarily for documents Web searches were more evenly distributed