Building usage contexts from interaction history
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Building Usage Contexts from Interaction History
Chris ParninCarsten Görg
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Information Worker Environments
Distractions Email, Meetings, Personal Events Work fragmentation Task switching
Costs Resumed tasks difficult, take twice as long. 40% of tasks not resumed. 57% of tasks interrupted. 62% recovering interruptions serious problem.
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Research Question
How can we support software development in a distracting and multi-tasked environment?
Can we support the ability of a developer to: Maintain elements of interest Recommend elements of interest
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Task-Switching Approaches
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Light-weight Visualization: Mylar
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Recommendation System: FAN
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Our Contributions
More formal interest model. Experimental framework for
evaluating models of interest, and recommendations.
Provide suggestions for tool designers to better accommodate developers.
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How Do Developer’s Express Interest?
Code Editor Edit Copy/Paste Click
Navigation Command Select
Other Query Inspect
inspect
commands
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Maintaining elements of interest
AAABABAAAAAXYACCDDADABAACDBA
{A,B,C,D,X,Y}
If we captured elements of interest Which elements are most interesting? How many elements to choose from?
Interaction History Event Stream
Usage Context
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Recommend elements of interest
Can we recommend elements of interest? Are we accurate? Useful? Do opportunities to give
recommendations actually exist?
{A,B,C} {D}
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Case Study
Visual Studio Plug-in collects Event, Element Name, Timestamp
Data 10 Government Employees 30 days of interactions.
Evaluation Cache of most interesting elements Recommendation Systems
NEXT – FAN like system. ASSOC
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Evaluating Interest Models
AAAABBCBAEFB A
CacheInteractions
Algorithm
LFU
LFU (decay)
FIFO
LRU
OPT
Cache Replacement
How to replace least interesting element?
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Interest Models Performance
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Intensity Slide
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Evaluating Recommendations
ABACXACXCXCXCY YBXAXACYACXCYCX
A
C
B.33
.66
X.90
Y.10
k-most likely recommendations
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NEXT Recommendation Performance
0
2
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6
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14
2 4 6 8 10
recovery
explore
total
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Why did we get this results?
Recommendation design Each state generates suggestions Short lifetime of recommendation
Transition patterns Recent access is strong feature. 95% transitions to previous methods. Small opportunity to make predictions.
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Improved Design
We can recover previously visited elements with cache.
We inject recommendations into cache. Clearly mark as recommendations. Extends lifetime of recommendation.
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NEXT+ Results
0
5
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45
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0 2 4 6 8 10 12
recovery
explore
total
total*
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Conclusions (Advice)
Design tools for work environments.
Record interactions when possible to evaluate tools.
Study the nature of how tasks are performed.
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Performance on High Intensity
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
OPT
OPT
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Number of methods interacted
Distribution of Size of Context in a Day
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
18.0%
20.0%
17 35 52 70 87 105 122 140 157 175 192 210 227 245 262 280 297 315 332
Number of Methods
Per
cen
tag
e
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Interactions in Day
Interactions in Day
0
500
1000
1500
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3000
9 10 11 12 1 2 3 4 5
Clicks
Commands
Edits
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Interactions in Week
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Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Clicks
Commands
Edits
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Edit/Navigation Ratio
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
0 2 4 6
B
C
D
E
F
G
H
I
J
K
A
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