Building usage contexts from interaction history
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Transcript of Building usage contexts from interaction history
Building Usage Contexts from Interaction History
Chris ParninCarsten Görg
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.
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
Task-Switching Approaches
Light-weight Visualization: Mylar
Recommendation System: FAN
Our Contributions
More formal interest model. Experimental framework for
evaluating models of interest, and recommendations.
Provide suggestions for tool designers to better accommodate developers.
How Do Developer’s Express Interest?
Code Editor Edit Copy/Paste Click
Navigation Command Select
Other Query Inspect
inspect
commands
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
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}
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
Evaluating Interest Models
AAAABBCBAEFB A
CacheInteractions
Algorithm
LFU
LFU (decay)
FIFO
LRU
OPT
Cache Replacement
How to replace least interesting element?
Interest Models Performance
Intensity Slide
Evaluating Recommendations
ABACXACXCXCXCY YBXAXACYACXCYCX
A
C
B.33
.66
X.90
Y.10
k-most likely recommendations
NEXT Recommendation Performance
0
2
4
6
8
10
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14
2 4 6 8 10
recovery
explore
total
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.
Improved Design
We can recover previously visited elements with cache.
We inject recommendations into cache. Clearly mark as recommendations. Extends lifetime of recommendation.
NEXT+ Results
0
5
10
15
20
25
30
35
40
45
50
0 2 4 6 8 10 12
recovery
explore
total
total*
Conclusions (Advice)
Design tools for work environments.
Record interactions when possible to evaluate tools.
Study the nature of how tasks are performed.
Performance on High Intensity
0
5
10
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25
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40
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
OPT
OPT
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
Interactions in Day
Interactions in Day
0
500
1000
1500
2000
2500
3000
9 10 11 12 1 2 3 4 5
Clicks
Commands
Edits
Interactions in Week
0
500
1000
1500
2000
2500
3000
Monday Tuesday Wednesday Thursday Friday Saturday Sunday
Clicks
Commands
Edits
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