Taste or Addiction?: Using Play Logs to InferSong Selection Motivation
Kosetsu Tsukuda(B) and Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST),
Tsukuba, Japan{k.tsukuda,m.goto}@aist.go.jp

Abstract. Online music services are increasing in popularity. They
enable us to analyze people’s music listening behavior based on playlogs. Although it is known that people listen to music based on topic(e.g., rock or jazz), we assume that when a user is addicted to an artist,s/he chooses the artist’s songs regardless of topic. Based on this assumption, in this paper, we propose a probabilistic model to analyze people’s
music listening behavior. Our main contributions are three-fold. First, tothe best of our knowledge, this is the ﬁrst study modeling music listening
behavior by taking into account the inﬂuence of addiction to artists. Second, by using real-world datasets of play logs, we showed the eﬀectivenessof our proposed model. Third, we carried out qualitative experiments andshowed that taking addiction into account enables us to analyze music
listening behavior from a new viewpoint in terms of how people listen tomusic according to the time of day, how an artist’s songs are listened toby people, etc. We also discuss the possibility of applying the analysis
results to applications such as artist similarity computation and song
recommendation.

1

Introduction

Among various leisure activities such as watching movies, reading books, andeating delicious food, listening to music is one of the most important for people [14]. In terms of the amount of accessible music, the advent of online music
services (e.g., Last.fm1 , Pandora2 , and Spotify3 ) has made it possible for people
to access millions of songs on the Internet, and it has become popular to playmusic using such services rather than physical media like CDs [8]. When users
play music online, such services record personal musical play logs that show
when users listen to music and what they listen to.
Since personal music play logs have become available, it has become popular
to use session information to analyze and model people’s music listening behavior [2, 4, 13, 18]. Here, a session is a sequence of logs within a given time frame.
Zheleva et al. [18] were the ﬁrst to model listening behavior using a topic model1

http://www.last.fm.
http://www.pandora.com.
3
http://www.spotify.com.
2c Springer International Publishing AG 2017

J. Kim et al. (Eds.): PAKDD 2017, Part II, LNAI 10235, pp. 721–733, 2017.
DOI: 10.1007/978-3-319-57529-2 56

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K. Tsukuda and M. Goto

based on session information. They revealed that a user tends to choose songs ina session according to the session’s speciﬁc topic such as rock or jazz. However,
it is not always correct to assume that a user chooses songs according to thesession’s topic. For example, after a user buys an artist’s album or temporarily
falls in love with an artist, s/he will be addicted to the artist and repeatedly
listen to the artist’s songs regardless of topic.
In light of the above, this paper proposes a model that can deal with botha session topic and addiction to artists. Our proposed model uses the model
proposed by Zheleva et al. [18] as the starting point. We present each songlistening instance in terms of the corresponding song artist. In our model, eachuser has a distribution over topics that reﬂects the user’s usual taste in music
and a distribution over artists that reﬂects the user’s addiction to artists. In
addition, each user has a diﬀerent ratio between usual taste and addiction, andprobabilistically chooses a song in a session based on this ratio. That is, if auser has a high addiction ratio, s/he will probably choose a song of an artist
from his/her artist distribution for addiction. Modeling people’s music listening
behavior by considering addiction is worth studying from various viewpoints:– Our model can show topic characteristics (e.g., the rock topic has a high ratioof addiction) and artist characteristics (e.g., most users choose an artist’s songs
when addicted to that artist). It is important to understand such characteristics from the social scientiﬁc viewpoint.
– Our model can also show user characteristics (e.g., a user chooses songs basedon addiction in a session). There are many applications that could use thisdata such as advertisements and recommendation systems. For example, if auser chooses songs of an artist based on addiction in a session, it would be
useful to recommend songs of that artist; if s/he chooses songs based on a
topic, it would be better to recommend other artists’ songs in the same topic.
Our main contributions in this paper are as follows.
– To the best of our knowledge, this is the ﬁrst study modeling music listening
behavior by considering both the usual taste in music and the addiction toartists.
– We quantitatively evaluated our model by using real-world music play logsof two music online services. Our experimental results show that the model
adopting both factors achieves the best results in terms of the perplexity
computed by using test data.
– We carried out qualitative experiments in terms of user characteristics, artist
characteristics, and topic characteristics and show that our model can be used
to analyze people’s music listening behavior from a new viewpoint.
The remainder of this paper is organized as follows. Section 2 presents related
work on analyzing music play logs and on modeling music listening behavior.
Section 3 describes the model that extends the model by Zheleva et al. [18]by considering the addiction phenomenon. Section 4 presents a procedure toinfer the parameters. Sections 5 and 6 report on our quantitative and qualitative
experiments, respectively. Finally, Sect. 7 concludes this paper.

Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

2

Related Work

2.1

Analysis of Music Listening Behavior

723

Analyzing people’s music listening behavior has attracted a lot of attention
because (1) understanding how people listen to music is important from thesocial scientiﬁc viewpoint and (2) the analysis results can give useful insight into
various applications such as music player interfaces and recommender systems.
People’s music listening behavior has been analyzed from various viewpoints. Rentfrow and Gosling [14] carried out a questionnaire-based survey andrevealed the correlations between music preferences and personality, self-views(e.g., wealthy and politically liberal), and cognitive ability (e.g., verbal skills andanalytical skills). Renyolds et al. [15] made an online survey and reported thatenvironmental metadata such as the user’s activity, weather, and location aﬀect
the user’s music selection. Analysis by Berkers [3] using Last.fm play logs showed
the signiﬁcant diﬀerences between male and female in terms of their music genre
preferences. More recently, Lee et al. [10] collected responses from users of commercial cloud music services and reported the criteria for generating playlists:
personal preference, mood, genre/style, artists, etc. Among various factors, time
information has received a lot of attention. Herrera et al. [6] analyzed play counts
from Last.fm and discovered that a non-negligible number of listeners listen tocertain artists and genres at speciﬁc moments of the day and/or on certain daysof the week. Park and Kahng [12] used log data of a commercial online music
service in Korea and showed that there existed seasonal and time-of-day eﬀects
on users’ music preference. Baur et al. [2] also showed the importance of seasonal
aspects, which inﬂuence music listening, using play logs from Last.fm.
In spite of the variety of listening behavior analyses, to the best of our knowledge, no work has focused on users’ addiction to, for example, songs and artists.
In this work, we deal with this factor and analyze people’s music listening behavior from a new perspective.
2.2

Application Based on Music Listening Logs

Listening logs have been used for various applications, including the detection ofsimilar artists. Schedl and Hauger [17] crawled Twitter4 for the hash tag #nowplaying and computed artist similarity using co-occurrence-based methods. Theirexperimental results showed that listening logs can be used to derive similarity
measures for artists. Another application is playlist generation. Liu et al. [11]proposed a playlist generation system informed by time stamps of a user’s listening logs in addition to the user’s music rating history and audio features suchas wave forms. The most popular application is music recommendation. Since
personal music play logs have become available, it has become popular to use
session information to recommend songs. Park et al. [13] proposed Session-based
Collaborative Filtering (SSCF), which extends traditional collaborative ﬁltering4

http://twitter.com/.

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K. Tsukuda and M. Goto

techniques by using preferred songs in the similar session. Dias and Fonseca [4]proposed temporal SSCF, where for each session, a feature vector is createdconsisting of ﬁve properties including time of day and song diversity. The work
closest to ours is that of Zheleva et al. [18], who proposed a statistical model
to describe patterns of song listening. They showed that a user tends to choose
songs in a session according to the session’s speciﬁc topic. We will describe thedetails of their model in Sect. 3.2.
Although none of these applications used addiction information, we believethat this information could improve the usefulness of these applications. We
discuss the possibility of using our analysis results to improve these applicationsin Sect. 6.

3

Model

As was mentioned earlier, our model builds on the one proposed by Zheleva
et al. [18]. After summarizing the notations used in our model in Sect. 3.1, we
ﬁrst describe the model by Zheleva et al. [18] in Sect. 3.2 and then propose ourmodel in Sect. 3.3.
3.1

Notations

Given a music play log dataset, let U be a set of users in the dataset. Let
lun = (u, a, tun ) denote the nth play log of u ∈ U . More speciﬁcally, user u plays
a song of artist a ∈ A at time tun . Here, A is the set of artists in the dataset.
Without loss of generality, we assume that play logs are sorted in ascendingorder of their timestamps: tun < tun for n < n .
To capture user’s listening preferences over time, we divide user’s play logs
into sessions. Following Zheleva et al. [18] and Baur et al. [2], we use the time
gap approach to generate sessions. If the gap between tun and tun+1 is less than
30 min, lun and lun+1 belong to the same session; otherwise, they belong to
diﬀerent sessions. Let Sur be the rth session of u where Sur consists of one ormore of u’s logs. Let Ru be the total number of u’s sessions; then the set of u’susessions is given by Du = {Sur }R
r=1 . Hence, the set of sessions of all users is given
by D = {Du }u∈U .
3.2

Session Model

The model proposed by Zheleva et al. [18], which is called the session model, is a
probabilistic graphical model based on the Latent Dirichlet Allocation (LDA) [1].
The session model assumes that for each session, there is a latent topic (e.g., rockor love song) that guides the choice of songs in the session. Figure 1(a) shows
the graphical model of the session model, where shaded and unshaded circles
represent observed and unobserved variables, respectively. In the ﬁgure, K is
the number of topics, Vur is the number of logs in the rth session of u, θ is theuser-topic distribution, and φ is the topic-artist distribution. We assume that θTaste or Addiction?: Using Play Logs to Infer Song Selection Motivation

725

Fig. 1. Graphical models of (a) session model and (b) session with addiction model.

and φ have Dirichlet priors of α and β, respectively. The generative process ofthe session model is as follows:– For each topic k ∈ {1, · · · , K}, draw φk from Dirichlet(β).
– For each user u in U ,• Draw θu from Dirichlet(α).
• For each session Sur in Du ,∗ Draw a topic zur from Categorical(θu ).
∗ For each song in Sur , observe an artist aurj from Categorical(φzur ).
In the generative process, aurj represents the jth song’s artist in the rth session
of u.
3.3

Session with Addiction (SWA) Model

Although Zheleva et al. [18] reported the usefulness of generating played songs
based on a session’s topic, we hypothesize that users can choose a song independently of topic. For example, after a user buys an artist’s album or temporarily
falls in love with an artist, s/he will repeatedly listen to the artist’s songs regardless of the topic. In other words, the user can be addicted to some artists. In suchan addiction mode, we assume that the user directly chooses a song without going
through the topic.
In light of the above, our model takes both session-topic-based and addictionbased choices of songs. Figure 1(b) shows the graphical model of our proposedmodel. Each user has a Bernoulli distribution λ that controls the weights ofinﬂuence for a session topic and addiction. To be more speciﬁc, when user uchooses a song in a session, we assume that the choice is inﬂuenced by thesession topic with probability λu0 (x = 0) and by u’s addiction to the artist withprobability λu1 (x = 1), where λu0 + λu1 = 1. When x = 0, a song is generated
through the same process of the session model, while when x = 1, a song isdirectly generated from a user-artist distribution ψ. The generative process of
the SWA model is as follows:

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K. Tsukuda and M. Goto

– For each topic k ∈ {1, · · · , K}, draw φk from Dirichlet(β).
– For each user u in U ,• Draw θu from Dirichlet(α).
• Draw ψu from Dirichlet(γ).
• Draw λu from Beta(ρ).
• For each session Sur in Du ,∗ Draw a topic zur from Categorical(θu ).
∗ For each song in Sur ,· Sample x from Bernoulli(λu ).
· If x = 0, observe an artist aurj from Categorical(φzur ).
· If x = 1, observe an artist aurj from Categorical(ψu ).

4

Inference

To learn the parameters of our proposed model, we use collapsed Gibbs sampling [5] to obtain samples of hidden variable assignment. Since we use a Dirichletprior for θ, φ, and ψ and a Beta prior for λ, we can analytically calculate the marginalization over the parameters. The marginalized joint distribution of D, latent
Vur Ruuvariables Z = {{zur }R
r=1 }u∈U , and latent variables X = {{{xurj }j=1 }r=1 }u∈U
is computed as follows:
P (D, Z, X|α, β, γ, ρ)=P (D, Z, X|Θ, Φ, Ψ , Λ)P (Θ|α)P (Φ|β)P (Ψ |γ)P (Λ|ρ)dΘdΦdΨ dΛ,(1)where Θ = {θu }u∈U , Φ = {φk }Kk=1 , Ψ = {ψu }u∈U , and Λ = {λu }u∈U . By
integrating out those parameters, we can compute Eq. (1) as follows:
P (D, Z, X|α, β, γ, ρ)
|U | 
|U |  
Γ(ρ + Nu0 )Γ(ρ + Nu1 ) Γ(γ|A|)Γ(2ρ)
a∈A Γ(Nu1a + γ)
=
2
|A|
Γ(ρ)
Γ(2ρ + Nu )
Γ(Nu1 + γ|A|)
Γ(γ)
u∈U
u∈U

K 
|U |  K

K 
Γ(αK)
Γ(β|A|)a∈A Γ(Nka + β)
k=1 Γ(Ruk + α)
(2)
×
.
K
|A|
Γ(Nk + β|A|)Γ(α)Γ(Ru + αK)
Γ(β)
u∈U
k=1

Here, Nu0 and Nu1 are the number of u’s logs such that x = 0 and x = 1,
respectively, and Nu = Nu0 + Nu1 . The term Nu1a represents the number oftimes that user u chooses artist a’s song under the condition of x = 1, and
Nu1 = a∈A Nu1a . Furthermore, Nk = a∈A Nka where Nka is the number oftimes artist a is assigned to topic k under the condition of x = 0. Finally,RukK
is the number of times u’s session is assigned to topic k, and Ru = k=1 Ruk .
For the Gibbs sampler, given the current state of all but one variable zur ,the new latent assignment of zur is sampled from the following probability:
P (zur = k|D, X, Z\ur , α, β, γ, ρ)∝ Γ(Nka\ur + Nura + β)Γ(Nk\ur + β|A|)
Ruk\ur + α,
Ru − 1 + αK Γ(Nk\ur + Nur + β|A|)Γ(Nka\ur + β)
a∈A

(3)

Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation727

where \ur represents the procedure excluding the rth session of u. Moreover,Nur and Nura represent the number of logs in rth session of u and the number
of a’s logs in rth session of u, respectively.
In addition, given the current state of all but one variable xurj , the probability
at which xurj = 0 is computed as follows:
P (xurj = 0|D, X\urj , Z, α, β, γ, ρ) ∝

ρ + Nu0\urj Nzur aurj \urj + β,
2ρ + Nu − 1 Nzur \urj + β|A|(4)where \urj represents the procedure excluding the jth song in the rth sessionof u. Similarly, the probability at which xurj = 1 is computed as follows:
P (xurj = 1|D, X\urj , Z, α, β, γ, ρ) ∝

ρ + Nu1\urj Nu1aurj \urj + γ.
2ρ + Nu − 1 Nu1\urj + γ|A|

(5)

Finally, we can make the point estimates of the integrated out parameters as
follows:θuk =Cuk + α,Cu + αK
λu0 =

φka =

Nka + β,Nk + β|A|

Nu0 + ρ,Nu + 2ρ

λu1 =

ψua =
Nu1 + ρ,Nu + 2ρ

Nu1a + γ.
Nu1 + γ|A|

(6)
(7)

where remind that λu0 and λu1 represent the ratio of usual taste in music andaddiction when u chooses songs, respectively.

5

Quantitative Experiments

In this section, we answer the following research question based on our quantitative experimental results: is adopting two factors, which are users’ daily taste
in music and addiction to artists, eﬀective to model music listening behavior?
5.1

DatasetTo examine the eﬀectiveness of the proposed model, we constructed two datasets.
The ﬁrst one is created from music play logs on a music download service
in Japan. On the service, users can buy a single song and an album and listen to them. For this evaluation, we obtained 10 weeks of log data between
1/1/2016 and 10/3/2016. We call this dataset JPD. The second one consistsof logs on Last.fm. To guarantee the repeatability, we used a publicly available
music play log data on Last.fm provided by Schedl [16]. Similar with JPD, we
extracted 10 weeks of log data between 1/1/2013 and 11/3/2013; we call thedataset LFMD.
From the 10 weeks of data of JPD, we created two pairs of training and test
datasets as follows. In the ﬁrst/second dataset, the training dataset consists oflogs of the ﬁrst four/eight weeks and the test dataset consists of the next two

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K. Tsukuda and M. Goto
Table 1. Statistics of our datasets
4WJPD

8WJPD

4WLFMD 8WLFMD

Number of users

7,230

13,986

2,501

2,850

Number of artists

3,441

6,431

7,899

12,360

Number of logs in training data

141,381

331,437

400,410872,614

Number of sessions in training data

35,78082,427

50,106

106,840

Number of logs in test data

48,837

57,126

179,983

201,966

Number of sessions in test data

11,767

13,51623,167

24,958weeks. For each dataset, we excluded artists whose songs were played by ≤3users and created session data as described in Sect. 3.1. Let the ﬁrst and second
dataset be 4WJPD (4W means four weeks) and 8WJPD, respectively. As for
LFMD, we also created two pairs of training and test datasets 4WLFMD and
8WLFMD in the same manner as we created the 4WJPD and 8WJPD datasets.
Table 1 shows the statistics of the four datasets.
5.2

Settings

In terms of hyperparameters, in line with other topic modeling work, we set
150
and β = |A|in the session model and the session with addiction (SWA)
α= K
50
model. In addition, in the SWA model, we set γ = |A|and ρ = 0.5.
To compare the performance of the session model and the SWA model, we
use the perplexities of the two models. Perplexity is a widely used measure tocompare the performance of statistical models [1] and the lower value represents
the better performance. The perplexity of each model on the test data is given by:
⎛ 
⎞
test
Rutest Vur
r=1
j=1 p(aurj ) ⎠
u∈U
,
(8)
perplexity(Dtest ) = exp ⎝−Rutest test
|V
|
ur
r=1
u∈U
test
represent the number of u’s sessions and the number ofwhere Rutest and Vurlogs in rth session of u in the test data, respectively. The p(aurj ) is computed
based on the estimated parameters obtained by Eqs. (6) and (7) as follows:Kp(aurj ) = λu0

θuk φkaurj + λu1 ψuaurj .

(9)

k=1

In terms of the number of topics, we compute the perplexity for K = 5, 10,
20, 30, 40, 50, 100, 200, and 300.
5.3

Results

Figure 2 shows the perplexity for each dataset. In any dataset, regardless of theamount of training data and the number of topics, the SWA model outperformed

Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

729

Fig. 2. Perplexity for 4WJPD, 8WJPD, 4WLFMD, and 8WLFMD.

the session model. If we set the number of topics to be larger than 300, the session
model might outperform the SWA model; but we set the maximum value of K to
300 for the following two reasons. The ﬁrst reason is due to the expended hours
for the learning process. For example, when the session model learns parametersfor K = 300 using 8WJPD, it takes 9.8 times longer than the SWA model doesfor K = 30 using 8WJPD (1,713 min for the session model and 175 min for the
SWA model). In data analysis, the expended hours is an important factor; if ittakes a long time to learn the parameters for a model, the model is inappropriate
for data analysis. The second reason is due to the understandability of topics.
When the number of topics becomes too large, it is diﬃcult to understand thediﬀerence between topics because there are many similar topics. As we will showin Sect. 6.3, analyzing the characteristics of each topic is useful to understandpeople’s music listening behavior. Hence, it is undesirable to set K to a large
value. For these reasons, we conclude that the SWA model is a better model
than the session model.

6

Qualitative Experiments

In this section, we report on the qualitative analysis results in terms of user
characteristics, artist characteristics, and topic characteristics. Due to the space
limitation, we only show the results for the training data of 8WJPD with K = 30.
We not only analyze people’s music listening behavior but discuss how we canapply the analysis results.

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K. Tsukuda and M. Goto

6.1

User Characteristics

As we mentioned in Sect. 3.3, each user has a parameter λ that controls the degreeof usual taste in music and addiction when s/he chooses songs. Given a user u,
we can obtain the ratio of these two factors from Eq. (7), where λu0 + λu1 = 1.
Figure 3(a) shows a histogram based on the degree of addiction. Although mostpeople put a high priority on their usual taste in music (ratio ≤ 0.1), the second
highest histogram peak is for those who put the greatest weight on addiction
to artists (ratio > 0.9). The result where so many users lie somewhere between
these two extremes of behavior further indicates the usefulness of considering
the addiction mode in music listening behavior.
By using the posterior distribution of latent variables in Eqs. (4) and (5),we can analyze the relationship between the degree of addiction and the time.
We ﬁrst analyzed the transition of the degree of addiction on a per-hour basis.
For example, to analyze the degree between 9:00:00 and 9:59:59, we collected
all play logs during the time period in the training data. By summing p(x = 0)of all logs, we can obtain the strength of usual taste in music during the time
period. Similarly, by summing p(x = 1) of all logs, we can obtain the strength of
addiction during the time period. Finally, we normalize their sum to 1 so that we
can see the ratio of the degree of the two factors. The left line chart in Fig. 4 shows
the results. It can be observed that the degree of addiction is high in the earlymorning (i.e., at 5, 6, and 7 am), while it is low at night (i.e., at 9, 10, and 11 pm).

(a)Frequency of users

7000

(b)

5000

6000

4000

5000
4000

3000

3000

2000

2000
1000

1000
0

0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1

Fig. 3. Histogram based on ratio of addiction among (a) users and (b) artists.
0.75

0.65
0.64

0.7

0.63
0.65
0.62
0.6

0.61

0.55

0.6
0

2

4

6

8

10

12

14

16

18

20

22

Mon Tue Wed Thu

Fri

Sat

Sun

Fig. 4. Time-dependent ratio of addiction: per-hour analysis result and per-weekday
analysis result.

Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

731

We can estimate that people tend to be short on time in the morning, and as aresult, they listen to a speciﬁc artist’s songs rather than choosing various songs
according to a topic. On the other hand, at night, people have time to spareand tend to listen to various artists’ songs by choosing from a topic. These
results indicate that the transition of the degree of addiction on a per-hour basis
enables us to analyze people’s music listening behavior from a new viewpoint.
In addition, we propose applying the knowledge to music recommendation. For
example, it would be more appropriate to recommend unknown songs to theuser at night rather than in the morning because s/he would have time to try
listening to new songs.
In the same manner as the above analysis, we also analyzed the transitionof the degree of addiction on a day of the week basis. The right line chart in
Fig. 4 shows the result. It can be observed that the degree of addiction is highon weekdays, while it is low on weekends. We can also estimate that the degreeof addiction is high on weekdays because people are busy working on weekdays,
while the degree is low on weekends because people have more time. These results
would also be useful to recommend music.
6.2

Artist Characteristics

In the same way as Sect. 6.1, given an artist, by summing p(x = 0) and p(x = 1)of all the artist’s logs, we can obtain the strength of usual taste and addiction
during the time period, respectively. Then their sum is normalized to 1 to compute the ratio of each factor of the artist. Figure 3(b) shows a histogram based on
the degree of addiction. It can be observed that most artists have a high degreeof addiction. From these results, we can estimate whether the artist’s songs arerepeatedly played by users who are enthusiastic admirers of the artist or by various users who listen to the artist’s songs with other artists’ songs. In addition,
we believe that the results could be used as one of the features to compute thesimilarity between artists by assuming that similar artists have similar degrees
of addiction.
6.3

Topic Characteristics

Finally, we show that our model can also be used for topic analysis. Given a
topic k, we collected representative artists in the category. To be more speciﬁc,the top 20 artists in terms of φk were extracted. For each of the 20 artists,
we collected all logs in the training data and computed the ratio of the degreeof taste in music and addiction as described in Sect. 6.1. We then computed
the average values of each degree over 20 artists and normalized their sum to 1.
Figure 5 shows the ratio of 30 topics, where topics are sorted in ascending order of
addiction ratio. As can be seen, the ratio between two factors is largely diﬀerent
from one topic to another: the addiction ratio ranged from 0.297 (10th topic) to0.620 (17th topic). As for the low addiction topics, the 10th topic has the lowest
value of 0.297. This topic is related to songs created by using VOCALOID [9],which is popular singing synthesizer software in Japan. The 8th topic has the732

K. Tsukuda and M. Goto

Fig. 5. Ratio of taste in music and addiction for each topic.

second lowest value of 0.334 and its topic is related to anime songs. From theseresults, we can estimate that when people listen to music related to popular
culture, they tend to listen to various artists’ songs in the topic. As for thehigh addiction topic, the 17th topic, which is related to Western artists, and the28th topic, which is related to old Japanese artists, have the highest values of
0.620 and 0.592, respectively. These results indicate the possibility of applying
the knowledge to playlist generation. In topics with a high addiction degree, itwould be useful to generate a playlist that consists of songs of a speciﬁc artist;
while in topics with a low addiction degree, it would be useful to generate aplaylist that consists of various artists’ songs.

7

Conclusion

In this paper we proposed a probabilistic model for analyzing people’s music
listening behavior. The model incorporates the user’s usual taste in music andaddiction to artists. Our experimental results using real-world music play logs
showed that our model outperformed an existing model that considers only theuser’s taste in terms of perplexity. In our qualitative experiments, we showed theusefulness of our model in various aspects: time-dependent play log analysis (e.g.,
the degree of addiction is high in the early morning and on weekdays), topicdependent play log analysis (e.g., the degree of addiction is low in an anime song
topic), etc.
For future work, we are interested in applying the knowledge obtained fromlog analysis to applications such as artist similarity computation and song recommendation as discussed in Sect. 6. We are also interested in extending ourmodel by considering the time transition of addiction. For example, a user who is
addicted to some artists in summer may be addicted to largely diﬀerent artists inautumn. Considering such time dependency by using the topic tracking model [7]is one possible direction to take to extend our model.
Acknowledgements. This work was supported in part by ACCEL, JST.

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References
1. Airoldi, E.M., Blei, D.M., Fienberg, S.E., Xing, E.P.: Mixed membership stochastic
blockmodels. J. Mach. Learn. Res. 9, 1981–2014 (2008)
2. Baur, D., Büttgen, J., Butz, A.: Listening factors: a large-scale principal components analysis of long-term music listening histories. In: CHI, pp. 1273–1276 (2012)
3. Berkers, P.: Gendered scrobbling: listening behavior of young adults on last.fm.
Interact. Stud. Commun. Culture 2(3), 279–296 (2012)
4. Dias, R., Fonseca, M.J.: Improving music recommendation in session-based collaborative ﬁltering by using temporal context. In: ICTAI, pp. 783–788 (2013)
5. Griﬃths, T.L., Steyvers, M.: Finding scientiﬁc topics. PNAS 101(Suppl. 1), 5228–
5235 (2004)
6. Herrera, P., Resa, Z., Sordo, M.: Rocking around the clock eight days a week:
an exploration of temporal patterns of music listening. In: WOMRAD, pp. 7–10
(2010)
7. Iwata, T., Watanabe, S., Yamada, T., Ueda, N.: Topic tracking model for analyzing
consumer purchase behavior. In: IJCAI, pp. 1427–1432 (2009)
8. Kamalzadeh, M., Baur, D., Möller, T.: A survey on music listening and management behaviours. In: ISMIR, pp. 299–305 (2012)
9. Kenmochi, H., Ohshita, H.: Vocaloid - commercial singing synthesizer based on
sample concatenation. In: INTERSPEECH, pp. 4009–4010 (2007)
10. Lee, J.H., Kim, Y.-S., Hubbles, C.: A look at the cloud from both sides now: ananalysis of cloud music service usage. In: ISMIR, pp. 299–305 (2016)
11. Liu, N.-H., Hsieh, S.-J., Tsai, C.-F.: An intelligent music playlist generator basedon the time parameter with artiﬁcial neural networks. Expert Syst. Appl. 37(4),2815–2825 (2010)12. Park, C.H., Kahng, M.: Temporal dynamics in music listening behavior: a casestudy of online music service. In: ACIS-ICIS, pp. 573–578 (2010)
13. Park, S.E., Lee, S., Lee, S.G.: Session-based collaborative ﬁltering for predicting
the next song. In: CNSI, pp. 353–358 (2011)
14. Rentfrow, P., Gosling, S.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236–1256(2003)
15. Reynolds, G., Barry, D., Burke, T., Coyle, E.: Interacting with large music collections: towards the use of environmental metadata. In: ICME, pp. 989–992 (2008)
16. Schedl, M.: The lfm-1b dataset for music retrieval and recommendation. In: ICMR,
pp. 103–110 (2016)17. Schedl, M., Hauger, D.: Mining microblogs to infer music artist similarity andcultural listening patterns. In: WWW, pp. 877–886 (2012)
18. Zheleva, E., Guiver, J., Mendes Rodrigues, E., Milić-Frayling, N.: Statistical models
of music-listening sessions in social media. In: WWW, pp. 1019–1028 (2010)