5. Music Listening Histories
User 1
Artist 1, Song 1, Timestamp
Artist 2, Song 3, Timestamp
Artist 1, Song 2, Timestamp Time
Artist 3, Song 5, Timestamp
Artist 4, Song 4, Timestamp
…
User 2
…
18. Main issues
o More concerned about design and aesthetics
o Static visualizations and only overviews of
listening histories
o Lack of interactive browsing and filtering
o Scalability issues, regarding the number of
songs to represent
19. Main issues
o More concerned about design and aesthetics
o Static visualizations and only overviews of
listening histories
o Lack of interactive browsing and filtering
o Scalability issues, regarding the number of
songs to represent
20. Main issues
o More concerned about design and aesthetics
o Static visualizations and only overviews of
listening histories
o Lack of interactive browsing and filtering
o Scalability issues, regarding the number of
songs to represent
21. Main issues
o More concerned about design and aesthetics
o Static visualizations and only overviews of
listening histories
o Lack of interactive browsing and filtering
o Scalability issues, regarding the number of
songs to represent
45. Setup Procedure
o 45 min for each test
o User’s personal computers
mytudut@flickr
46. Test Session Procedure
1. Quick introduction
2. Application description
3. Practice Time
4. Tasks execution
5. Satisfaction survey & Informal Interview
47. Tasks
9 tasks
2 Categories
1. Explore and browse the listening history
2. Pattern detection and trends on music listening
habits
48. Exploring Tasks: examples
Indicate the most played song of the artist X
over the last three months
Describe the trend on the previous identified
song
49. Exploring Tasks: examples
Indicate the most played song of the artist X
over the last three months
Describe the trend on the previous identified
song
50. Pattern Detection Tasks: examples
Describe and try to justify the listening changes
that occurred over the last three months
Describe the listening habits on the selected
period
51. Pattern Detection Tasks: examples
Describe and try to justify the listening changes
that occurred over the last three months
Describe the listening habits on the selected
period
70. Life Past Events
User 1
“Here I was working on a scientific paper,
because I was listening only classical music, and
I like to hear that kind of music when I'm
writing, But then I skipped listening to music,
because I had some project discussions, and no
time to listen to music”
71. Profile Inference
User 2
"Well, do you know why this part of the
visualization contains mostly recent music, even
though I just prefer to listen old music?“
72. Profile Inference
User 2
"Well, do you know why this part of the
visualization contains mostly recent music, even
though I just prefer to listen old music?“
one of his favorite artists just released a
new album after years of absence
73. Hidden Time Habits
User 4
"I did not realized that I was listening too much
music in late night, but now that I think of this, I
usually listen to more rhythmic music at that
time to stay awake a little longer, mostly when
I'm working"
74. Listening Trends
User 3
"Looks like that through a regular day I keep
changing the genre of music I listen to. I start
with something stronger in the morning and
then end the day with more relaxing songs!"
76. Timeline
o Timeline-based mechanism proved to be a
major asset:
– Main browsing and filtering technique
– Effectiveness and flexibility validated by
experimental results
77. Context Information
o Considered to be an important aspect of the
solution
– Info about most played elements
– Acts as a visual clue to start the exploration
78. “Age of Songs”
o Can effectively convey information about the
listening habits
– Different profiles by direct color inspection
79. Knowledge analysis and inference
o Performed by combining insights from the
different techniques
o Possible main based on time, absence of
music listening and context information
81. Conclusions
Novel solution for exploring and filtering
listening histories
1. Combines a timeline-based visualization with a
set of synchronized-views to perform direct
exploration
2. Introduces a new feature: the “age” of songs
3. Allows listening pattern detection, only based on
time and some textual metadata
82. Future Work
o Data mining on listening histories data
– Discover new hidden listening patterns
– New pattern examples:
• Users that always seek the tops
• Others that enjoy mostly female voices
• Listen to classic music in the morning but rhythmic
music in the afternoon
o Map the new patterns to this and other
visualizations
