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Analysis of the Changes in Listening Trends of a Music Streaming Service

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

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Analysis of the Changes in Listening Trends of a Music Streaming Service

  1. 1. Dec. 11 2017 - 2nd International Workshop on Application of Big Data for Computational Social Science CyberAgent, Inc. All Rights Reserved 0 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. 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. 3. Music Streaming Service AWA 2 • Japanese music streaming service like spotify, Apple music, Google Play, etc.
  4. 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. 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. 6. Related Work: Hierarchical User Analysis [Kawazu, 2016] 5 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. 7. Approach: Hierarchically Adaptation of HMM 6 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. 8. Data-sets 7 • 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. 9. Typical Artists on Each Hidden State of 1st Layer 8 • 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. 10. Examples of detecting the changes of user listening trends 9 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. 11. The Effect of the Changes of Listening Trends on User Motivation 10 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. 12. Results 11 • Major changes of music listening trends decreased user usage frequency (y) • Minor changes of music listening trends increased user usage frequency(y)
  13. 13. When do users change their listening trends? 12 • 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. 14. How do change their listening trends? 13 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. 15. Summary 14 • 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. 16. Summary 15 • 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.

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