Finding and Re-Finding Through Personalization Jaime Teevan MIT, CSAIL David Karger (advisor), Mark Ackerman, Sue Dumais, Rob Miller (committee), Eytan

Finding and Re-Finding Through Personalization

Jaime Teevan

MIT, CSAIL

David Karger (advisor), Mark Ackerman, Sue Dumais, Rob Miller (committee), Eytan Adar, Christine Alvarado, Eric Horvitz, Rosie Jones, and Michael Potts

Thesis Overview

• Supporting Finding– How people find– Individual differences affect finding– Personalized finding tool

• Supporting Re-Finding– How people re-find– Finding and re-finding conflict– Personalized finding and re-finding tool

Old

New

Thesis Overview

• Supporting Finding– How people find– How individuals find– Personalized finding tool


Supporting Re-Finding

• How people re-find– People repeat searches– Look for old and new

• Finding and re-finding conflict– Result changes cause problems

• Personalized finding and re-finding tool– Identify what is memorable– Merge in new information

Supporting Re-Finding

• How people find– People repeat searches– Look for old and new



Query log analysis

Memorability study

Re:Search Engine

Related Work

• How people re-find– Know a lot of meta-information [Dumais]

– Follow known paths [Capra]

• Changes cause problems re-finding– Dynamic menus [Shneiderman]

– Dynamic search result lists [White]

• Relevance relative to expectation [Joachims]

Query Log Analysis

• Previous log analysis studies– People re-visit Web pages [Greenberg]

– Query logs: Sessions [Jones]

• Yahoo! log analysis– 114 people over the course of a year– 13,060 queries and their clicks

• Can we identify re-finding behavior?

• What happens when results change?

Re-Finding Common

Repeat query

Repeat clickUnique click

40% 86%

33%

87% 38%

26%

of queries of queries

of queriesof queries

of repeat queries

of repeat queries

Change Reduces Re-Finding

• Results change rank

• Change reduces probability of repeat click– No rank change: 88% chance– Rank change: 53% chance

• Why?– Gone?– Not seen?– New results are better?

Change Slows Re-Finding

• Look at time to click as proxy for Ease

• Rank change slower repeat click– Compared with initial search to click– No rank change: Re-click is faster– Rank change: Re-click is slower

• Changes interfere with re-finding

?

Old

New

“Pick a card, any card.”

Case 1 Case 2 Case 3 Case 4 Case 5 Case 6

Your Card is GONE!

People Forget a Lot

Change Blindness

Change Blindness

Old

New

We still need magic!

Memorability Study

• Participants issued self-selected query

• After an hour, asked to fill out a survey

• 129 people remembered something

Memorability a Function of Rank

00.10.20.30.40.50.60.70.8

1 2 3 4 5 6 7 8 9 10

Rank - R

P(R

emem

|R,C

)

Clicked - C Not clicked

Remembered Results Ranked High

-2

0

2

4

6

8

10

12

-2 0 2 4 6 8 10 12

Actual Rank

Rem

embe

red

Ran

k

Old

New

result list 1

result list 2

…

result list n

Re:Search Engine Architecture

User client

Web browser

MergeIndex of past queries

Result cache

Search engine

User interaction cache

query result list

query 1

query 2

…

query n

score 1

score 2

…

score n

result list

Components of Re:Search Engine

• Index of Past Queries

• Result Cache

• User Interaction Cache

• Merge Algorithm

Index of past queries

queryquery 1

query 2

…

query n

score 1

score 2

…

score n

result list 1

result list 2

…

result list n

Result cache

query 1

query 2

…

query n

User interaction cache

result list 1

result list 2

…

result list n

Merge result list

result list

Index of Past Queries

• Studied how queries differ– Log analysis– Survey of how people remember queries

• Unimportant: case, stop words, word order

• Likelihood of re-finding deceases with time

• Get the user to tell us if they are re-finding– Encourage recognition, not recall

Index of past queries

queryquery 1

query 2

…

query n

score 1

score 2

…

score n

Merge Algorithm

• Benefit of New Information score– How likely new result is to be useful…– …In a particular rank

• Memorability score– How likely old result is to be remembered…– …In a particular rank

• Chose list maximizes memorability and benefit of new information

result list 1

result list 2

…

result list n

Merge result list

result list

Benefit of New Information

• Ideal: Use search engine score

• Approximation: Use rank

• Results that are ranked higher are more likely to be seen– Greatest benefit given to highly ranked results

being ranked highly

Memorability Score

• How memorable is a result?

• How likely is it to be remembered at a particular rank?

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1 2 3 4 5 6 7 8 9 10

-2

0

2

4

6

8

10

12

-2 0 2 4 6 8 10 12

Choose Best Possible List

• Consider every combination

• Include at least three old and three new

• Min-cost network flow problem

…

…

…

…10

7

7

10

m2

m1

m10

b10

b2

b1

st

Old

New

Slots

Old

New

Evaluation

• Does merged list look unchanged?– List recognition study

• Does merging make re-finding easier?– List interaction study

• Is search experience improved overall?– Longitudinal study

List Interaction Study

• 42 participants

• Two sessions a day apart – 12 tasks each session

• Tasks based on queries• Queries selected based on log analysis

– Session 1– Session 2

• Re-finding• New-finding

(“stomach flu”)

(“Symptoms of stomach flu?”)

(“Symptoms of stomach flu?”)(“What to expect at the ER?”)

List Interaction Study

New 1

New 2New 3New 4

New 5New 6

Old 5New 1Old 1Old 7New 2New 3New 4Old 4New 5New 6

Old

New

Experimental Conditions

• Six re-finding tasks– Original result list– Dumb merging– Intelligent merging

• Six new-finding tasks– New result list– Dumb merging– Intelligent merging

Old

New




Old 1Old 2Old 4New 1New 2New 3New 4New 5New 6Old 10

Old 1Old 2Old 4

Old 10

Measures

• Performance– Correct– Time

• Subjective– Task difficulty– Result quality




Faster, fewer clicks, more correct answers, and easier!

Similar to Session 1

Results: Re-Finding

Performance Original Dumb Intelligent

% correct 96%

Time (seconds)

99% 88%

38.7 45.670.9

Results: Re-Finding

Subjective Original Dumb Intelligent

% correct 99% 88% 96%

Time (seconds) 38.7 70.9 45.6

Task difficulty 1.57

Result quality 3.61 3.42 3.70

1.531.79

Results: Re-Finding

Original Dumb Intelligent

% correct 99% 88% 96%

Time (seconds) 38.7 70.9 45.6

Task difficulty 1.57 1.79 1.53


List same?

• Intelligent merging better than Dumb

• Almost as good as the Original list

Similarity

60% 76%76%

Results: New-Finding

Performance New Dumb Intelligent

% correct 73% 74% 84%

Time (seconds) 139.3 120.5153.8


Subjective New Dumb Intelligent

% correct 73% 74% 84%

Time (seconds) 139.3 153.8 120.5


Result quality 3.193.38 2.94


New Dumb Intelligent

% correct 73% 74% 84%

Time (seconds) 139.3 153.8 120.5



List same?

• Knowledge re-use can help

• No difference between New and Intelligent

Similarity

38% 50% 61%

Results: Summary

• Re-finding– Intelligent merging better than Dumb– Almost as good as the Original list

• New-finding– Knowledge re-use can help– No difference between New and Intelligent

• Intelligent merging best of both worlds

Conclusion

• How people re-find– People repeat searches– Look for old and new



Future Work

• Improve and generalize model– More sophisticated measures of memorability– Other types of lists (inboxes, directory listings)

• Effectively use model– Highlight change as well as hide it

• Present change at the right time– This talk’s focus: what and how– What about when to display new information?

Thesis Overview

• Supporting Finding– How people find– How individuals find– Personalized finding tool


David Karger (advisor), Mark Ackerman, Sue Dumais, Rob Miller (committee), Eytan Adar, Christine Alvarado, Eric Horvitz, Rosie Jones, and Michael Potts

Thank You!

Jaime Teevan

[email protected]

Documents

Finding and Re-Finding Through Personalization Jaime Teevan MIT, CSAIL David Karger (advisor), Mark Ackerman, Sue Dumais, Rob Miller (committee), Eytan