Presentation at 2nd International Workshop on Application of Big Data for Computational Social Science (workshop at IEEE BigData2017)

1. Dec. 11 2017 - 2nd International Workshop on Application of Big Data for Computational Social Science
CyberAgent, Inc. All Rights Reserved
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Analysis of the Changes in
Listening Trends of a Music
Streaming Service
M. Takano1, H. Mizukami1, F. Toriumi2,
M. Takeuchi1, K. Wada1, M. Yasuda1, and I. Fukuda1
1CyberAgent, Inc.
2University of Tokyo

2. Music Streaming Services
1
• These services provide millions of music listening tracks
as subscription services.
• Users can easily access a large amounts of music.
・i.e., users have enormous choices.
• To provide a comfortable experience, it is important to
understand how user select listening to music.

3. Music Streaming Service AWA
2
• Japanese music streaming service
like spotify, Apple music, Google Play, etc.

4. Changes of music listening trends
3
• We enjoy music in various situations
• Some events change our listening trends
・We may change listening trends
- if we find a good singer in TV at last night
- if our friends share their favorite singers in Twitter
→ Detection these changes of music listening trends
is important for understanding user behavior.

5. Purpose and Approach
4
• Evaluation of changes of music listening trends
・Do the changes give good experience?
i.e., Do users enjoy the changes? Do they continue using the service?
・How do the good changes happen?
・Does music streaming services drive the good changes?
• In general, we can interpret human behaviors at
various scales.
E.g. in music listening,
・The most micro scale: tracks
・The most macro scale: genres (POP, ROCK, etc.)
→ Hierarchical modeling

6. Related Work: Hierarchical User Analysis [Kawazu, 2016]
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Pag
e
A
Page
B
Page
C
Page
C
Page
C
Leave
User’s
latent state P
User’s
latent state Q
Session type X
Hidden Markov Model
Clustering
Page
D
Page
D
Session
type X
Session
type Y
Session
type X
•••Session
type Z
Leave
Clickstream
Sequences of
latent state
Session
cluster
Abstraction Level
User
••• •••
••• •••
••• •••
Bottom-up Approach

7. Approach: Hierarchically Adaptation of HMM
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Top-down Approach
1. Creating HMM as a first layer
2. Creating HMMs as a second layer about each state of the first layer
• Each hidden status is a listening trend
・First layer: macro trends
・Second layer: micro trends
• The number of hidden states of each HMM was selected based on AIC
Female Male

8. Data-sets
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• Time series data for HMM
・Series of the most frequent artist of user at each hour
[{“MM/DD/YYYY-HH”: Artist}, …]
・E.g.: [{“05/11/2017-10”: QUEEN}, {“5/11/2017-11”: QUEEN},
{“5/11/2017-17”: KISS}, {“5/13/2017-11”: Metarica}, {“5/13/2017-23”, Helloween}, …]
• Data for Creating Models
・March 01 2017 〜 May 10 2017（71 days）
・Heavy users who were sampled at random
・N = 2,189
• Data for Evaluating Models
・April 01 2017 〜 May 21 2017（50 days）
・Heavy and middle users who were sampled at random
・N = 19,929

9. Typical Artists on Each Hidden State of 1st Layer
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• 1: ?
• 2, 3: Western music
• 4: Rap
• 5: Korean POP
• 6, 7: Japanese POP singer (women)
• 8: Japanese POP singer (men)
• 9: EXILE (project name)
• 10: Japanese POP group
• 11: Band and Idol singer

10. Examples of detecting the changes of user listening trends
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2−1
2−2
2−3
4 01 4 15 5 01 5 15
Datetime
State
1−2
1−3
1−4
2−1
2−2
2−3
2−4
2−5
3−5
3−7
4−4
4 01 4 15 5 01 5 15
Datetime
State
Stable listening trend Unstable listening trend
• This user mainly listened to music in
the hidden state of 2 in 1st layer.
• The user only had 3 minor changes
in 2 months.
• This user frequently change their listening
trends.
• The user have 4 major hidden states (1, 2, 3,
4), and 11 minor hidden states.

11. The Effect of the Changes of Listening Trends on User Motivation
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Do the changes in their listening trends affect a userʼs motivation?
Model
・Objective Variable: Usage frequency the week after (y)
・Explanatory Variable:
・s : the number of changes on first layer
・s': the number of changes on second layer
・m: the length of time series
co-variate to control the frequency of usage of AWA
+β0

12. Results
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• Major changes of music listening trends decreased
user usage frequency (y)
• Minor changes of music listening trends increased
user usage frequency(y)

13. When do users change their listening trends?
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• Transition ratio of hidden states for each day of the week
(Black: 1st, Red: 2nd)
• Weekday morning: users often changed their trends
• Midnight: users didnʼt tend to change their trends
• 1st layer correlate 2nd layer
Fri Sat Sun
Mon Tue Wed Thu
0 5 10 15 20 0 5 10 15 20 0 5 10 15 20
0 5 10 15 20
0.0
0.1
0.2
0.3
0.4
0.0
0.1
0.2
0.3
0.4
Time
NumberofStateChange(Mean)
Weekday

14. How do change their listening trends?
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The mean number of changes to the
listening trends for each features
• User changed their trends by using
their created lists
• Search and related X didnʼt change
user trends
• Recommendation was fifth
• These features changed both
user trends.
・i.e., we cannot control the changes
of user listening trends by using
these features

15. Summary
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• We analyzed the user behaviors based on the
changes in listening trends.
• The trends were extracted by applying the HMM
hierarchically.
• Findings: to increase user motivation,
・small changes in listening trends are good
・large changes in listening trends are bad

16. Summary
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• When and how did users change their trends?
・during their commute time
・by user-created lists
• With these changes, the 1st layer was similar to the 2nd
layer.
・That is, when the service drives good changes in the
trends, it also tends to drive bad changes.
→ The need for a recommendation algorithm that only
drives good changes in listening trends.
・By using of user-created lists,
services cannot coordinate user behaviors.
・On the other hand, recommendations can be controlled
in details through the use of algorithms.