Improvise presentation at the CBRecSys Workshop in San Mateo, CA.

1. AUser-centeredMusic RecommendationApproachforDaily Activities
Ricardo Dias, Ricardo Cunha, Manuel J. Fonseca
INESC-ID/Instituto Superior Técnico, Universidade de Lisboa

2. Music listening on daily activities
Mark Stevens@Flickr

3. Listening experience is ubiquitous
clemsonunivlibrary@flickr

5. > 20 Million Songs

6. Hard to choosesongs
Orin Zebest@flickr

7. Different users, Different tastes
Chloe Muro@flickr

8. How to recommendsongs?
Jacob Bøtter@flickr

9. Neglecting User Control

10. Improvise
davejdoe@flickr

11. User-centered approach
mammal@flickr

12. RELATED WORK

13. Different Approaches
Demographic
Collaborative Filtering
Content
Context
Hybrid
[Celma]
[Celma2010]

14. Collaborative Filtering
Song1
Song2
Song3
Song4
Song5
Alice
5
3
4
4
?
User1
3
1
2
3
3
User2
4
3
4
3
5
User3
3
3
1
5
4
User4
1
5
5
2
1

15. Content

16. MoyanBrenn@flickr
Context

17. Peter Merholz@flickr
Where are the users?

18. •Text
Goals
1.Generic recommendationtechnique that takes userinputfor personalization
2.Recommend songs to be listened performing different activities

19. IMPROVISE

20. Recommend songs for Activities

21. abethne15@flickr
User-centered

22. the rikpics@flickr
Personalization

23. Approach
Song1
Song1
Song1
Song1
Song1
Song1
Song1
Song5
Song2
Song2
Song2
Song3
Activity 1
Activity 2
Activity 3

24. giuvanpelt@flickr
Content for Similarity

25. ASSOCIATING SONGS WITH ACTIVITIES

26. •Text
Web Application

27. •Text
Selection Flow
15 Genres
30 Artists of the selected genres
100 songs from those artists

28. •Text
Data Collected
98 participants
251 answers to the activities
8,518 songs associated to the activities

29. •Text
Song Characterization
Accousticness
Energy
Loudness
Tempo
4D vector of content features

30. Ourania@flickr
Analyze to detect similarities

31. One feature, one interval
Tempo
Max
Min
[Min; Max]

32. Hyper-Rectangles
Running
Relaxing
Shopping

33. One Hyper-Rectangle, 4 Features
Tempo
Accoustiness
Energy
Loudness

34. •Text
One to One
One Activity
One Hyper-rectangle

35. •Text
Main Challenge
Defining the hyper-rectangles
Establishing the min/max values for each one the four intervals

36. HYPER-RECTANGLES

37. Nick Wheeler@flickr
How to choosethe best method?

38. •Text
Classification Task
Activity 2
Activity 3
Activity 2
Song3

39. •Text
Testing Procedure
Two datasets for training classifier: top- 100 and the top-20
Hyper-rectangles determined using the different methods

40. •Text
First Trial
M1 -AVG +/-STD for the min/max of each interval
M2 –10%/90% percentiles for min/max

41. Running
Relaxing
Shopping
Intervals overlapped

42. •Text
Second Trial
M3 -% of the STD (15% to 30%) + AVG
M4 -% of the STD (15% to 30%) + Median
Increments of 5% from 15% to 30%

43. Top-100 songs dataset
Median +/-20% of the STD
Best Approach

44. GENERIC RECOMMENDER

45. Cold-start Approach

46. Top-100 songs selected by the 98 users for each activity
4 hyper-rectangles generated using the Median +/-20% of the STD
Generic Model

47. PERSONALIZATION

48. Greg Peverill-Conti@flickr
How about individual tastes?

49. Greg Peverill-Conti@flickr
Method
Same as in Generic Model
Top 100 songs
Median +/-20% of STD

50. Greg Peverill-Conti@flickr
Add new songs
List of Songs
Top
Bottom
New Song

51. Greg Peverill-Conti@flickr
First In, First Out
Top
Bottom
Top
Bottom
In
In
Out
Out
Song
Song
Time

52. Greg Peverill-Conti@flickr
Benefits
1.New songs remain more time in the list to determine the hyper-rectangles
2.Constantlyadapts to users current preferences and tastes

53. EVALUATION

54. 1.Recommend songs to different activities
2.Personalizethe recommendations to users with different tastes
Goals

55. 2 F / 8 M
Aged between 22 and 29years old
Students(graduate and undergraduate from computer science)
Participants

56. 1.Generic model: 10 users
2.Personalized model (two iterations)
1.First iteration: 10 users
2.Second iteration: 5 users
Two Experiments

57. PROCEDURE

58. Characterize users:
•Gender
•Age
•Listening Habits (frequency)
•Etc.
Demographic Questionnaire

59. Song Selection
Selecting appropriate songs from a list of 50 songs (one list per activity)
Evaluation Task

60. Satisfaction Questionnaire

61. Nr. of songs
Metric

62. RESULTS

63. LambrosRoussodimos@flickr
Happy users

64. yourtecontemporaine@flickr
Personalized better than Generic

65. Generic Model
12
38
0
5
10
15
20
25
30
35
40
Selected
NotSelected
Number of Songs

66. Generic Model
12
38
0
5
10
15
20
25
30
35
40
Selected
NotSelected
Number of Songs
24%

67. Personalized Model (1º)
12
38
15
35
0
5
10
15
20
25
30
35
40
Selected
NotSelected
Personalized
Generic

68. Personalized Model
12
38
15
35
0
5
10
15
20
25
30
35
40
Selected
NotSelected
Personalized
Generic
25%

69. Personalized Model (2º)
10%increase

70. Satisfaction Results

71. Special cases
User 1
User 2
User 3
User 4
User 5

72. Discussion
For some users more interaction is required to improve recommendation
Might improveby using more songs than just the top 100

73. Discussion
For some users more interaction is required to improve recommendation
Might improveby using more songs than just the top 100

74. Number of songs steadily increased

75. LIMITATIONS

76. Lack of comparison
No comparison between the Generic Recommenderand other solutions

77. Elvin@flickr
Few Users

78. Peter Merholz@flickr
Activities were simulated

79. CONCLUSIONS AND FUTURE WORK

80. Recommend songs for Daily Activities

81. Silvia Viñuales@flickr
User-centered Approach

82. ca_heckler@flickr
Experimental Evaluation

83. Kat@flickr
More evaluation required

84. Explore newmethods

85. Use moresongs
> 8.000

86. AUser-centeredMusic RecommendationApproachforDaily Activities
Ricardo Dias, PhdStudent and Researcher
http://web.tecnico.ulisboa.pt/ricardo.dias
The End