Interactive Exploration ofMusic Listening HistoriesRicardo Dias, Manuel J. Fonseca,Daniel Gonçalves
Context & Motivation
Proliferation of lifelogging services
Tracking Listening Habits
Music Listening HistoriesUser 1  Artist 1, Song 1, Timestamp  Artist 2, Song 3, Timestamp  Artist 1, Song 2, Timestamp   T...
Profile Generation
Recommendation
Pattern detection in user habits                             zachstern@flickr
Good at recording data…
… and allow direct access to it!
But Visualization is also important!                              ottonassar@flickr
Related work
StreamGraph [Byron and Wattenber 2008]
Tangle, Strings and Knots [Baur2009]
LastHistory [Baur2011]
Fans effort to visualize dataScrobbling Timeline                    Arc diagrams    LastGraph
Problems
Main issueso More concerned about design and aestheticso Static visualizations and only overviews of  listening historieso...
Main issueso More concerned about design and aestheticso Static visualizations and only overviews of  listening historieso...
Main issueso More concerned about design and aestheticso Static visualizations and only overviews of  listening historieso...
Main issueso More concerned about design and aestheticso Static visualizations and only overviews of  listening historieso...
Our solution
Design principlesRationale
Exploration              thegiantvermin@flickr
Overview           [Dias and Fonseca2010]
Interactivity                Ultrastart
Details on demand                    m4tik@flickr
Inference            mterraza@sxc
Analysis
Music Listening History ExplorerMULHER
MULHER
Main Visualization: Stacked dot
Main Visualization: Timeline
Main Visualization: Background
Filteringo Genreo Artist name    Single selectiono Free Text                                    Multiple Selectiono Time  ...
StatisticsGeneral Statistics     Daily and Hourly Visualizations                       With Context   Without Context
Brushing & Highlighting
ImplementationRegular Web Application   Backend Server HTML, CSS, Javascript      Protovis             Java backend JSON t...
Demo
Evaluation
Two Experiments                  swamibu@flickr
First experimentCommon listening historyEvaluate:  – UI usability  – Users satisfaction    and experience
Second Experiment: Case Studieso Users exploring their own listening historieso Listening Patterns Detection
Experiments DetailsTest Procedure         Users     Tasks        Listening Logs     =                 ≠
Setup Procedureo 45 min for each testo User’s personal computers                              mytudut@flickr
Test Session Procedure1.   Quick introduction2.   Application description3.   Practice Time4.   Tasks execution5.   Satisf...
Tasks9 tasks2 Categories  1. Explore and browse the listening history  2. Pattern detection and trends on music listening ...
Exploring Tasks: examplesIndicate the most played song of the artist Xover the last three monthsDescribe the trend on the ...
Exploring Tasks: examplesIndicate the most played song of the artist Xover the last three monthsDescribe the trend on the ...
Pattern Detection Tasks: examplesDescribe and try to justify the listening changesthat occurred over the last three months...
Pattern Detection Tasks: examplesDescribe and try to justify the listening changesthat occurred over the last three months...
First Experiment
Common Logo Listening Histories from one of the authorso 5.000 records (11/2009 to 07/2011)                               ...
Participants                          8        2N = 10Ages between 20-50 yearsListen to music almost every day
Occupations4 CS students2undergraduate CS students  2 SW engineers         1 journalist1 HS student
Statistical Evaluationo Effectivenesso Error rateo Satisfaction                                   iamwahid@sxc
First ExperienceResults
Effectiveness100%
Overall Task Success Rate  97%               3%Successfull      Unsuccessfull
Problem of Task 3# Users   10                    6   0          T1   T2   T3   T4   T5   T6   T7   T8   T9
Overall Tasks Easiness                           1      8         1  0             0 Very        Difficult   Normal   Easy...
User Experience Satisfaction    Fantastic   Rewarding     Stimulant   Easy   Flexible987654321    Horrible    Frustrating ...
Second Experiment
Users’ logso Personal listening historieso From 3.000 to 30.000 records                                 alaasafei@sxc
Participants: Second Experience                      #N = 4Ages between 20-40 yearsAll listen to music every dayActive Las...
Occupations2 CS Assistant Teachers  1 Senior Software Engineer     1 CS PhD Student
Results
Identical Statistical Results                          lumaxart@flickr
Pattern Inference – Users interviews                                whitebeard@sxc
Life Past EventsUser 1“Here I was working on a scientific paper,because I was listening only classical music, andI like to...
Profile InferenceUser 2"Well, do you know why this part of thevisualization contains mostly recent music, eventhough I jus...
Profile InferenceUser 2"Well, do you know why this part of thevisualization contains mostly recent music, eventhough I jus...
Hidden Time HabitsUser 4"I did not realized that I was listening too muchmusic in late night, but now that I think of this...
Listening TrendsUser 3"Looks like that through a regular day I keepchanging the genre of music I listen to. I startwith so...
Discussion
Timelineo Timeline-based mechanism proved to be a  major asset:  – Main browsing and filtering technique  – Effectiveness ...
Context Informationo Considered to be an important aspect of the  solution  – Info about most played elements  – Acts as a...
“Age of Songs”o Can effectively convey information about the  listening habits  – Different profiles by direct color inspe...
Knowledge analysis and inferenceo Performed by combining insights from the  different techniqueso Possible main based on t...
Conclusions
ConclusionsNovel solution for exploring and filteringlistening histories  1. Combines a timeline-based visualization with ...
Future Worko Data mining on listening histories data  – Discover new hidden listening patterns  – New pattern examples:   ...
Thank youQuestions?ricardo.dias@ist.utl.ptweb.ist.utl.pt/~ricardo.dias
MULHER@AVI2012
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Transcript of "MULHER@AVI2012"

  1. 1. Interactive Exploration ofMusic Listening HistoriesRicardo Dias, Manuel J. Fonseca,Daniel Gonçalves
  2. 2. Context & Motivation
  3. 3. Proliferation of lifelogging services
  4. 4. Tracking Listening Habits
  5. 5. Music Listening HistoriesUser 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 …
  6. 6. Profile Generation
  7. 7. Recommendation
  8. 8. Pattern detection in user habits zachstern@flickr
  9. 9. Good at recording data…
  10. 10. … and allow direct access to it!
  11. 11. But Visualization is also important! ottonassar@flickr
  12. 12. Related work
  13. 13. StreamGraph [Byron and Wattenber 2008]
  14. 14. Tangle, Strings and Knots [Baur2009]
  15. 15. LastHistory [Baur2011]
  16. 16. Fans effort to visualize dataScrobbling Timeline Arc diagrams LastGraph
  17. 17. Problems
  18. 18. Main issueso More concerned about design and aestheticso Static visualizations and only overviews of listening historieso Lack of interactive browsing and filteringo Scalability issues, regarding the number of songs to represent
  19. 19. Main issueso More concerned about design and aestheticso Static visualizations and only overviews of listening historieso Lack of interactive browsing and filteringo Scalability issues, regarding the number of songs to represent
  20. 20. Main issueso More concerned about design and aestheticso Static visualizations and only overviews of listening historieso Lack of interactive browsing and filteringo Scalability issues, regarding the number of songs to represent
  21. 21. Main issueso More concerned about design and aestheticso Static visualizations and only overviews of listening historieso Lack of interactive browsing and filteringo Scalability issues, regarding the number of songs to represent
  22. 22. Our solution
  23. 23. Design principlesRationale
  24. 24. Exploration thegiantvermin@flickr
  25. 25. Overview [Dias and Fonseca2010]
  26. 26. Interactivity Ultrastart
  27. 27. Details on demand m4tik@flickr
  28. 28. Inference mterraza@sxc
  29. 29. Analysis
  30. 30. Music Listening History ExplorerMULHER
  31. 31. MULHER
  32. 32. Main Visualization: Stacked dot
  33. 33. Main Visualization: Timeline
  34. 34. Main Visualization: Background
  35. 35. Filteringo Genreo Artist name Single selectiono Free Text Multiple Selectiono Time Combining Filters
  36. 36. StatisticsGeneral Statistics Daily and Hourly Visualizations With Context Without Context
  37. 37. Brushing & Highlighting
  38. 38. ImplementationRegular Web Application Backend Server HTML, CSS, Javascript Protovis Java backend JSON to represent data
  39. 39. Demo
  40. 40. Evaluation
  41. 41. Two Experiments swamibu@flickr
  42. 42. First experimentCommon listening historyEvaluate: – UI usability – Users satisfaction and experience
  43. 43. Second Experiment: Case Studieso Users exploring their own listening historieso Listening Patterns Detection
  44. 44. Experiments DetailsTest Procedure Users Tasks Listening Logs = ≠
  45. 45. Setup Procedureo 45 min for each testo User’s personal computers mytudut@flickr
  46. 46. Test Session Procedure1. Quick introduction2. Application description3. Practice Time4. Tasks execution5. Satisfaction survey & Informal Interview
  47. 47. Tasks9 tasks2 Categories 1. Explore and browse the listening history 2. Pattern detection and trends on music listening habits
  48. 48. Exploring Tasks: examplesIndicate the most played song of the artist Xover the last three monthsDescribe the trend on the previous identifiedsong
  49. 49. Exploring Tasks: examplesIndicate the most played song of the artist Xover the last three monthsDescribe the trend on the previous identifiedsong
  50. 50. Pattern Detection Tasks: examplesDescribe and try to justify the listening changesthat occurred over the last three monthsDescribe the listening habits on the selectedperiod
  51. 51. Pattern Detection Tasks: examplesDescribe and try to justify the listening changesthat occurred over the last three monthsDescribe the listening habits on the selectedperiod
  52. 52. First Experiment
  53. 53. Common Logo Listening Histories from one of the authorso 5.000 records (11/2009 to 07/2011) alaasafei@sxc
  54. 54. Participants 8 2N = 10Ages between 20-50 yearsListen to music almost every day
  55. 55. Occupations4 CS students2undergraduate CS students 2 SW engineers 1 journalist1 HS student
  56. 56. Statistical Evaluationo Effectivenesso Error rateo Satisfaction iamwahid@sxc
  57. 57. First ExperienceResults
  58. 58. Effectiveness100%
  59. 59. Overall Task Success Rate 97% 3%Successfull Unsuccessfull
  60. 60. Problem of Task 3# Users 10 6 0 T1 T2 T3 T4 T5 T6 T7 T8 T9
  61. 61. Overall Tasks Easiness 1 8 1 0 0 Very Difficult Normal Easy Very easydifficult
  62. 62. User Experience Satisfaction Fantastic Rewarding Stimulant Easy Flexible987654321 Horrible Frustrating Boring Hard Rigid
  63. 63. Second Experiment
  64. 64. Users’ logso Personal listening historieso From 3.000 to 30.000 records alaasafei@sxc
  65. 65. Participants: Second Experience #N = 4Ages between 20-40 yearsAll listen to music every dayActive Last.fm account
  66. 66. Occupations2 CS Assistant Teachers 1 Senior Software Engineer 1 CS PhD Student
  67. 67. Results
  68. 68. Identical Statistical Results lumaxart@flickr
  69. 69. Pattern Inference – Users interviews whitebeard@sxc
  70. 70. Life Past EventsUser 1“Here I was working on a scientific paper,because I was listening only classical music, andI like to hear that kind of music when Imwriting, But then I skipped listening to music,because I had some project discussions, and notime to listen to music”
  71. 71. Profile InferenceUser 2"Well, do you know why this part of thevisualization contains mostly recent music, eventhough I just prefer to listen old music?“
  72. 72. Profile InferenceUser 2"Well, do you know why this part of thevisualization contains mostly recent music, eventhough I just prefer to listen old music?“ one of his favorite artists just released anew album after years of absence
  73. 73. Hidden Time HabitsUser 4"I did not realized that I was listening too muchmusic in late night, but now that I think of this, Iusually listen to more rhythmic music at thattime to stay awake a little longer, mostly whenIm working"
  74. 74. Listening TrendsUser 3"Looks like that through a regular day I keepchanging the genre of music I listen to. I startwith something stronger in the morning andthen end the day with more relaxing songs!"
  75. 75. Discussion
  76. 76. Timelineo Timeline-based mechanism proved to be a major asset: – Main browsing and filtering technique – Effectiveness and flexibility validated by experimental results
  77. 77. Context Informationo Considered to be an important aspect of the solution – Info about most played elements – Acts as a visual clue to start the exploration
  78. 78. “Age of Songs”o Can effectively convey information about the listening habits – Different profiles by direct color inspection
  79. 79. Knowledge analysis and inferenceo Performed by combining insights from the different techniqueso Possible main based on time, absence of music listening and context information
  80. 80. Conclusions
  81. 81. ConclusionsNovel solution for exploring and filteringlistening 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. 82. Future Worko 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 afternoono Map the new patterns to this and other visualizations
  83. 83. Thank youQuestions?ricardo.dias@ist.utl.ptweb.ist.utl.pt/~ricardo.dias

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