KIITE CAFE: A WEB SERVICE FORGETTING TOGETHER VIRTUALLY TO LISTEN TO MUSIC
Kosetsu Tsukuda
Keisuke Ishida
Masahiro Hamasaki
Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST), Japan{k.tsukuda, ksuke-ishida, masahiro.hamasaki, m.goto}@aist.go.jp
ABSTRACT
In light of the COVID-19 pandemic making it difficultfor people to get together in person, this paper describes
a public web service called Kiite Cafe that lets users get
together virtually to listen to music. When users listen tomusic on Kiite Cafe, their experiences are characterizedby two architectures: (i) visualization of each user’s reactions, and (ii) selection of songs from users’ favorite songs.
These architectures enable users to feel social connection
with others and the joy of introducing others to their favorite songs as if they were together in person to listen
to music. In addition, the architectures provide three user
experiences: (1) motivation to react to played songs, (2)the opportunity to listen to a diverse range of songs, and(3) the opportunity to contribute as curators. By analyzing the behavior logs of 1,760 Kiite Cafe users over about
five months, we quantitatively show that these user experiences can generate various effects (e.g., users react to amore diverse range of songs on Kiite Cafe than when listening alone). We also discuss how our proposed architectures can continue to enrich music listening experienceswith others even after the pandemic’s resolution.
1. INTRODUCTION
Unlike listening to music alone, listening to music withothers adds the qualities of feeling social connection andletting others listen to one’s favorite songs. For example,
the former quality occurs when attending a live concert andsharing the experience with other audience members [1,2], while the latter quality occurs when people introduce
others to their favorite songs [3–5].
These qualities have become hard to enjoy since the
COVID-19 pandemic has made it difficult to get togetherin person and listen to music with others. Instead of attending a live concert, people can listen to the same music at
the same time via TV, radio, or live streaming on the web.
However, such media provide a poor alternative because
the former quality of social connection requires audiences
to get together in the same place so that they can see eachother’s reactions to the music. Similarly, instead of directly
introducing others to favorite songs, people can post URL
links to them (e.g., YouTube videos of songs) to social net-

working services (SNSs) such as Twitter and Facebook.
Even if many SNS users react to a song post (e.g., witha “thumbs up”), there is no guarantee that they actually listened to the song and liked it. Rather, the latter quality ofsharing a favorite song with others requires knowing thatpeople who react actually listened to the song.
In light of the above, we propose a web service called
Kiite Cafe 1 2 that enables people to get together virtually
to listen to music without losing the above qualities. Kiite
Cafe is characterized by the following two architectures:(i) when users listen to songs on Kiite Cafe, each user’s reactions are visualized, and (ii) songs played on Kiite Cafe
are selected from users’ favorite songs. To facilitate an intuitive understanding of the user experiences provided bythese architectures, we give the following example.
Suppose that Emily is a Kiite Cafe user. One day, shelogs in to Kiite Cafe and finds that 14 users are logged in.
Each user is identified by his/her own icon. The users, including Emily, can simultaneously listen to the same song,which is automatically selected and played. Even if theplayed song has a different mood from songs that Emily
usually listens to, if she likes it, she can add it to her listof favorite songs (i.e., her favorites list). Because she has
encountered a new favorite song, she feels happy to listen
to a diverse range of songs. Moreover, when the currently
played song is added to her favorites list, architecture (i)visualizes her reaction by displaying a heart symbol on her
icon. Because other users’ reactions are also visualized,
she can see their reactions to feel social connection. For
example, one of Emily’s favorite songs is played when it
is automatically selected by architecture (ii). While her favorite song is playing, she is pleased to notice that a heart
symbol is displayed on another user’s icon. Then, otherusers also react to the song, and eventually the heart symbol is displayed on eight users’ icons. Architecture (i) thus
enables Emily to see the moments when other users start
liking one of her favorite songs. This experience makes
her feel happy and want other users to listen to another ofher favorite songs. Thus, Emily looks forward to anotherfavorite song being played; until then, she stays on Kiite
Cafe and enjoys other users’ favorite songs.
Our contributions can be summarized as follows.
• We propose two architectures for enabling people tosimultaneously listen to the same music online whileachieving the qualities of social connection and the joyof introducing other people to favorite songs.

© Kosetsu Tsukuda, Masataka Goto. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Kosetsu Tsukuda, Masataka Goto, “Kiite Cafe: A Web Service
for Getting Together Virtually to Listen to Music”, in Proc. of the 22ndInt. Society for Music Information Retrieval Conf., Online, 2021.

1 “Kiite” means “Listen” in Japanese.
2 https://cafe.kiite.jp697

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021
J Highly recommended list button

I Reasons why song was selected

A Video player

B Favorite button
F Comment balloon

C Heart symbolwith effect

G Delete button
H Rotation button

K Bot

D Comment text box

Default user icon

E Comment balloon button

Figure 1. Screenshot of Kiite Cafe.
• We implemented and released a web service, called Kiite Cafe, that applies these architectures.
• We describe three user experiences in which users (1)are motivated to react to songs, (2) can listen to a diverse range of songs, and (3) can contribute as curators;
we also discuss the effects of these experiences on users
as a result of the two proposed architectures.
• By analyzing logs of user behavior on Kiite Cafe, weshow that the architectures do provide the above effects.
Specifically, users (1) react to songs more actively as
the number of users on Kiite Cafe increases, (2) react
to a more diverse range of songs on Kiite Cafe thanwhen they listen to songs alone, and (3) stay on Kiite
Cafe longer when they contribute more as curators.
2. OVERVIEW OF KIITE CAFE
Kiite Cafe is implemented as a novel function on Kiite,
which is an existing web service for exploring and discovering music. Any Kiite user can use Kiite Cafe. Below, we
introduce Kiite’s functions related to Kiite Cafe and thengive an overview of Kiite Cafe.
2.1 Kiite
Song data on Kiite are routinely collected from Nico Nico
Douga, which is one of the most popular video sharing
services in Japan. On Nico Nico Douga, it is quite popular for both amateur and professional musicians to upload songs created with singing voice synthesizer software
called VOCALOID [6]. As of the end of Mar. 2021, morethan 220,000 songs can be played back on Kiite. When aKiite user listens to a song, its video clip is played on Kiite
by an embedded video player 3 .
Kiite enables users to effectively find favorite songs byproviding novel functions such as exploration of songs
based on their emotions and continuous listening to only
the choruses of multiple songs. A registered user can set
her own icon image, add songs to her favorites list, create
playlists, listen to other users’ playlists, and so on.
3 On Nico Nico Douga, all songs are uploaded as music videos.

698

2.2 Kiite Cafe
Fig. 1 shows an overview of Kiite Cafe. When a user logs
in, her icon is displayed at a random position in a twodimensional space that also displays other logged-in users.
All of the users listen to the same song played in a video
player ( A in the figure) at the same time, like a live concert. As mentioned in section 1, Kiite Cafe has two architectures, for visualizing users’ reactions and selecting
songs to play from users’ favorite songs. In the rest of thissection, we describe the details of each architecture.
2.2.1 Architecture (i): User Reaction Visualization
We visualize the following four kinds of reactions so thatusers can see each other’s reactions to a played song.
Favorite. When a user likes a played song, she can add
it to her favorites list by clicking the “favorite” button B .
When the button is clicked, a heart symbol with an animation effect is displayed at the top right of the user’s
icon while the song is playing ( C ). This enables users
to quickly see how many users like a song. When the user
had already added the played song to her favorites list, theheart symbol is displayed without the effect.
Comment. When a comment is entered in a text box D
and the “comment balloon” button E is clicked, a comment balloon is displayed above the user’s icon for 90 seconds ( F ). The user can also manually delete her commentby clicking the “delete” button G . Users can thus use thisfunction to express their impressions of a played song or
have simple communication with each other.
Rotation. A user can rotate her icon by clicking the“rotation” button H . The icon then rotates clockwise at a
uniform rate until the played song ends. The user can also
manually stop the rotation by clicking the “rotation” button
again. Users can use this function to express feelings likea sense of excitement. However, note that Kiite Cafe does
not provide any guidance on when users should use thisfunction, because we want them to use it as they please.
Move. By clicking an arbitrary position in the twodimensional space, a user can move her icon to the clickedProceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

position. The icon is animated to move to the position ina straight line at a uniform rate. Kiite Cafe does not display any meaning for the quadrants and axes in the twodimensional space. Instead, as with the Rotation function,
we leave the usage of the Move function to users.
2.2.2 Architecture (ii): Song Selection from Users’
Favorite SongsLet U denote a set of users who are logged in to Kiite Cafe.
For each user u, we define Su as the set of songs included
in u’sS
favorites list or playlists. A played song is selectedfrom u∈U Su . The automatic song selection process isinvoked before the end of the currently played song, and it
consists of the following two steps: (1) selection of a userand (2) selection of a song from the user’s favorite songs.
In the first step, if there are biases toward certain selected users, then the selected songs may also be biased.
Moreover, some users may become frustrated if their favorite songs are not selected at all. To avoid such biases
and satisfy every user, we developed an algorithm thatcan randomly but fairly select users and thus diversify theplayed songs. Suppose that user u is selected in the firststep, such that every user has an equal chance to be selected. When song s ∈ Su is randomly selected in thesecond step, the reason for its selection is displayed, e.g.,“This song is in u’s playlist” (first row of I in Fig. 1). If sis also among other users’ favorite songs, that information
is displayed (second and later rows of I ) so that thoseusers can notice that one of their favorite songs is being
played. Moreover, a user can set one of her playlists as a“highly recommended list” by clicking a button J . Whenthe selected user sets a list as “highly recommended,” asong in that list is randomly but preferentially selected in
the second step. By setting such a list, a user can specify
the songs that she wants other users to listen to.
Note that the implemented selection algorithm described above for our service is just an example, and otheralgorithms can be used as long as they balance the fairness
and randomness of selecting both users and songs.
In addition, we created a bot account K that is always
logged in. The bot periodically creates playlists according
to a daily/weekly popularity ranking of VOCALOID songson Nico Nico Douga. The bot is treated as one of the users,
and songs in its playlists can also be selected by the songselection process. This gives a user a chance to listen to
the latest popular songs and find new favorite songs evenwhen no other human users are logged in. Note that the bot
does not show any reactions to played songs.
3. USER EXPERIENCES AND EFFECTS
As mentioned in section 1, the proposed architectures add
two qualities: social connection and the joy of introducing
others to favorite songs. In addition, the Kiite Cafe architectures provide three kinds of user experiences. This section describes those experiences and their effects on users.
3.1 Motivation to React to Songs
Although many studies have been conducted on enablingusers to listen to music together, most of them have focused on visualizing the song selection process or propos-

ing methods for that process [7–11]. A system that canshow a summary of listeners’ feedback on a song (total
numbers of likes and dislikes) has been proposed [12];however, little attention has been paid to visualizing eachuser’s reactions. In contrast, Kiite Cafe visualizes users’
reactions via their icons, as in section 2.2.1. By sharing all
the users’ reactions with each other, Kiite Cafe motivatesthem to react to the currently played song. Accordingly,
we expect that, the more people get together on Kiite Cafe,
the more meaningful it will be to show their reactions, andthe more actively they will react to songs. In the long term,
this would enable users to develop the habit of actively listening to music and enrich their listening experiences [13].
3.2 Diversification of Song Listening
Many studies have sought to play songs that match the musical preferences of as many users as possible [7, 9, 14].
In the short term, this approach may be able to increaseusers’ satisfaction. In the long term, however, as is known
from the negative effects of a filter bubble [15, 16], thisapproach could narrow users’ musical interests. On theother hand, because Kiite Cafe plays songs selected fromvarious users’ favorite songs, it may not be able to always
match most users’ musical preferences. However, listening to a more diverse range of songs enables users to find
not only songs that match their preferences well but also
unexpected or serendipitous songs [17] that do not match
their usual preferences. In other words, we expect that auser will react to a more diverse range of songs on Kiite
Cafe than when she listens alone. In the long term, thisexperience would expand the user’s horizons.
3.3 Contribution as Curators
According to architecture (ii), suppose that a song in user
u’s playlist is selected and played on Kiite Cafe. Becauseof architecture (i), u can see the moment when other users
start liking or show interest in that song (e.g., u can see
when other users add the song to their favorites list or rotate their icon). In substance, for other users, u plays arole as a curator. That is, the two architectures enable every user to naturally contribute as a curator. We expectthat when a user experiences the joy of contributing as a
curator, she will look forward to the curation opportunity
when another of her favorite songs is played and thus increase her dwell time on Kiite Cafe. It has been reported
that acting as a curator increases music listening activity(e.g., listening to more songs and making playlists for curation) [18]. Therefore, in the long term, this experiencewould promote users’ daily music listening activity.
4. EXPERIMENT
We launched the Kiite Cafe service on Aug. 5, 2020. In thissection, we evaluate the three expected effects discussed in
the previous section. To this end, we analyzed user behavior logs for the period between Aug. 5, 2020 and Jan. 14,
2021. The number of unique users who logged in during
this period was 1,760. The Favorite, Comment, Rotation,and Move reactions were used 29,127, 9,826, 59,983, and45,353 times, respectively.
699

Normalized reaction proportion

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021
5

Reaction r
Favorite
Comment
Rotation
Move

Favorite
Comment
Rotation
Move

4
3
2
1

|U r |
130
56
118
110

avgdiv(Suorg )10.49310.38410.502
10.559

avgdiv(Sur )10.96010.92010.91811.050p-value
1.99 ×10−6
4.40 ×10−3
5.80 ×10−6
8.21 ×10−9

Table 1. Diversity of musical preferences.
1-5

6-10

Number of users

11-15

16-

Figure 2. Relation between the number of users on Kiite
Cafe and the normalized reaction proportion.
4.1 Frequency of User Reactions
As a result of users sharing their reactions with each otheron Kiite Cafe, we expect that they will be more motivated
to react as the number of users increases. To verify this effect, we evaluated the following research question: Does auser react to a played song more frequently as the numberof users on Kiite Cafe increases? (RQ1)
Settings. We considered the four kinds of reactions:
R = {Favorite, Comment, Rotation, Move}. First, for eachplayed song, we obtained Us , the set of users except the
bot who were on Kiite Cafe when song s started playing.
According to the number of users (i.e., |Us |), we categorized songs into four classes (C1 : 1 ≤ |Us | ≤ 5; C2 :6 ≤ |Us | ≤ 10; C3 : 11 ≤ |Us | ≤ 15; C4 : 16 ≤ |Us |).
To answer RQ1, for each reaction, we compared the average proportion of users who reacted to a song among theclasses. More formally, let SCi denote a list of songs in
Ci (1 ≤ i ≤ 4) 4 . Given song s ∈ SCi and reactionr ∈ R, let Usr denote the set of users who gave r as a reaction to s. Then, the proportion of such users is given by|U r |
ratio(s, r) = |Uss | . Finally, the average proportion overSCi was computed as follow.
X1
ratio(s, r).
avgratio(SCi , r) =|SCi |s∈SCi

Results. Fig. 2 shows the results. For visibility,
avgratio(SCi , r) was normalized by avgratio(SC1 , r) for
each reaction. For all the reactions, the reaction proportion
monotonically increased as the number of users increased;
thus, the answer to RQ1 is “Yes.” Because the Favorite
function was obviously used to add a song to a user’s favorites list, we discuss how the users used the other threefunctions. Regarding the Rotation function, although Kiite
Cafe does not explain its purpose, we searched Kiite Cafe
users’ tweets on Twitter 5 and found that a number of users
used it to express their feelings of excitement. Next, by
analyzing tweets about the Move function, we found that it
was used mainly for two purposes. First, users moved theiricons as if they were dancing. Second, users regarded the
top left of the two-dimensional space (i.e., near the video
player) as the front row at a live concert venue and moved
there when their favorite songs were played. It is interesting that such a culture was created by the users and spreadamong them. Finally, regarding the Comment function,
4 Because the same song can be played multiple times on Kiite Cafe,
the same song can appear multiple times in SCi .
5 We assumed that Twitter users who tweeted about the function were
Kiite Cafe users.

700

although the average length of the played songs was 237
seconds, 10.1% of comments were posted within the first
15 seconds of a song. In such comments, users often expressed the joy of having their favorite songs played (e.g.,“Come ooooon!” and “Yeeeees!”). This was similar to thephenomenon at live concerts in which the audience gets
excited when a favorite song starts. In summary, as thenumber of users increased, they were more likely to express their excitement and behave as if they were attending
a live concert.
4.2 Diversity of Reacted Songs
Because Kiite Cafe enables users to listen to songs that do
not always match their musical preferences, we expect thatthey will react to a more diverse range of songs. To verify
this effect, we evaluated the following research question:
Does a user react to a more diverse range of songs on Kiite
Cafe as compared to her musical preferences before she
started using the service? (RQ2)
Settings. Let tu denote the time when user u initiallylogged in to Kiite Cafe. We assumed that songs added tou’s favorites list before tu (i.e., before using Kiite Cafe),
denoted by Suorg , represented u’s original musical preferences; these were collected on the original Kiite service,
which was launched on Aug. 30, 2019, and described in
section 2.1. Moreover, we assumed that songs for whichu gave reaction r, denoted by Sur , represented u’s musicalpreferences in terms of r after starting to use Kiite Cafe. To
answer RQ2, we compared the diversity of Sur with that of
Suorg for each reaction. Formally, the diversity was computed as the intra-list diversity [19]. In the case of Suorg ,
P
P
org
org
si ∈Susj ∈Su\{si } dist(si , sj )org
div(Su ) =
,
org
org
|Su |(|Su | − 1)
where dist(si , sj ) is the Euclidean distance between thefeature vectors of si and sj . For the diversity, we obtained
a song’s feature vector by using OpenL3 [20]. For each reaction r, we considered only users who had more than nine
songs in both Suorg and Sur so that we could appropriately
measure the users’ musical preferences 6 . Let U r denotethe set of such users. Then, given r, the average diversityof Suorg was computed as
1 X
avgdiv(Suorg ) = r
div(Suorg ).
|U |
u∈U r
P
1
r
Similarly, avgdiv(Su ) = |U r | u∈U r div(Sur ).
Results. Table 1 lists the results. We can say that for all
reactions, the diversity of songs producing reactions statistically increased in comparison to the diversity of favorite
songs before starting to use Kiite Cafe; thus, the answer to
RQ2 is “Yes.” These results indicate that Kiite Cafe is also
6 Because we released a beta version of Kiite Cafe on May 1, 2020,
users who logged in to Kiite Cafe for the first time between May 1, 2020
and Aug. 4, 2020 were not included in this analysis.

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021
Favorite

3.5

Comment

1-5 (C1)
6-10 (C2)
11-15 (C3)
16- (C4)

3.0
2.5

2.0

2.0

1.5

1.5

1.0

1.0

0

0-0.250.25-0.5

0.5-

Rotation

3.0

1-5 (C1)
6-10 (C2)
11-15 (C3)
16- (C4)

2.5

2.5

2.5
2.0

1.5
0

0-0.250.25-0.5

0.5-

1.0

1-5 (C1)
6-10 (C2)
11-15 (C3)
16- (C4)

3.0

2.0
No data was observedfor "0.5-" of "11-15 (C3)."

Move

3.5

1-5 (C1)
6-10 (C2)
11-15 (C3)
16- (C4)

1.5
0

0-0.250.25-0.5

0.5-

1.0

0

0-0.250.25-0.5

0.5-

Figure 3. Relation between the proportion of users who gave reactions (x-axis) and the normalized dwell time (y-axis).
useful as a service for users to find songs that are different
from their daily musical preferences.
4.3 Dwell Time
Because Kiite Cafe enables users to experience the joyof contributing as curators, we expect that they will staylonger as their contributions increase. To verify this, weevaluated the following research question: Does a user
stay on Kiite Cafe for a longer time as the proportion ofusers who react to her favorite songs increases? (RQ3)
Settings. We define user u’s session on Kiite Cafe as
the duration between u’s login and logout times. In u’s
kth session, suppose that three of u’s favorite songs wereplayed, and that 0%, 40%, and 16% of users gave reaction
r to those songs. Following the assumption that the maximum percentage (in this case, 40%) influenced u’s dwelltime, we categorized the maximum value of ratio(s, r) (defined in section 4.1) into four classes (G1 : ratio(s, r) = 0;
G2 : 0 < ratio(s, r) ≤ 0.25; G3 : 0.25 < ratio(s, r) ≤ 0.5;
G4 : 0.5 < ratio(s, r)). However, for a proportion of
0.4, eight reacting users among 20 users would have ahigher impact on u than two reacting users among five
users. Therefore, we also considered the classes of |Us |as in section 4.1. That is, to answer RQ3, given a classof the number of users, we compared the average session
lengths between the reaction proportion classes for eachreaction. Formally, let Du denote a list of u’s sessions.
The kth session Tu,k ∈ Du represents a list of songs fromu’s favorite songs (Su ) that were played in the session. For
that session, we selected the song smaxu,k ∈ Tu,k that had
the highest proportion of users who gave reaction r (i.e.,smaxu,k = arg max ratio(s, r)). Given Ci and Gj , we defines∈Tu,kthe set of smaxu,k belonging to Ci and Gj in all users’ sesmax
sions as Si,j = {smaxu,k |u ∈ U ∧ 1 ≤ k ≤ |Du | ∧ su,k ∈max
max
Tu,k ∧ su,k ∈ Ci ∧ su,k ∈ Gj }. Let len(u, k) denote thelength in seconds of u’s kth session. Then, the average
session length was computed asX
1avglen(Si,j ) =len(u, k).
|Si,j | max
su,k ∈Si,j

Results. Fig. 3 shows the results; avglen(Si,j ) was normalized by avglen(Si,1 ) for each reaction for visibility. For
the Favorite, Rotation, and Move functions, we can seethat the dwell time tended to increase as the proportion ofusers who gave that reaction increased. In these graphs,
the line for the class of 1-5 users is located at the lowest
position among the four classes (C1 - C4 ). Especially for
Rotation and Move, at each reaction proportion, the normalized dwell time tended to increase with the number ofusers. These results indicate that not only the proportionof users who gave a reaction but also the absolute numberof such users influenced the dwell time. On the other hand,no clear tendency was observed for the Comment function
when the number of users was 6-10 or 11-15. Still, it ispossible that Comment also had a positive effect on thedwell time, because it monotonically increased when thenumber of users was 1-5 or at least 16. Detailed analysis
with more user behavior logs will be required to verify thiseffect, and we leave that for a future work. In summary, theanswer to RQ3 is “Yes” for Favorite, Rotation, and Move.
5. DISCUSSION
In section 3, we described the user experiences provided by
Kiite Cafe and their effects. We believe that Kiite Cafe haseven more potential to diversify and enrich users’ music
listening experiences. In this section, to demonstrate that
potential, we discuss three themes.
5.1 Application Examples for Online Events
Kiite Cafe has been used for several online events including VOCALOID-related events. At an event on Aug. 29,
2020, for example, a famous creator of VOCALOID songs
made a special playlist that consisted of songs that the creator liked or had created. During the one-hour event, asmany as 140 Kiite Cafe users enjoyed simultaneously listening to the songs in the playlist, and used the reaction
functions of Kiite Cafe to communicate with the creatorin real time. For another event on Feb. 11, 2021, a questionnaire was conducted on favorite songs related to winteror snow in the VOCALOID event. During the 90-minute
event, 77 users enjoyed listening to songs in a playlist created according to the questionnaire answers.
Although it has become difficult for people to get together in person and communicate with each other andwith artists because of the COVID-19 pandemic, we
demonstrated a new style of online music events throughthese examples. Moreover, even after the pandemic’s resolution, we believe that this kind of online event will bevaluable for users who cannot easily attend physical eventsfor reasons such as geographic remoteness.
5.2 Additional Service Functions
Although all users on Kiite Cafe get together in one online space, it would be interesting to provide additional
spaces for different purposes in the future (we could callthe main space and additional spaces the “main cafe” and“branches,” respectively). For example, for a branch on thetheme of “time,” we could put a higher priority on songs related to time (e.g., playing night-related songs at night) by701

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

analyzing song lyrics if they are available in the song selection process. We could also consider a function that allows
any user to conduct a questionnaire by displaying possible
responses in each quadrant of the two-dimensional space.
For example, a user might ask “Who would you like tolisten to the played song with?” and assign responses of“family,” “lover,” “friend,” and “other” to the quadrants.
Other users could answer this question by moving theiricons. This function would provide a good opportunity to
see how other users perceive a song.
5.3 Reusable Insights
The reusable insights can be summarized as follows.
• Through the experiments, we verified that the two architectures are effective in promoting users’ music listening activity. These architectures can be helpful forother researchers and companies to develop interfacesthat enable users to listen to music together.
• The examples of successful online events showed thatthe architectures can offer new ways to enjoy music with other people even during the COVID-19 pandemic. We thus opened up a new research theme to support interactions among creators, audiences, and music.
• We clarified the value of visualizing the moments whenusers start liking a song. In contrast to traditional curation on an SNS, the approach of Kiite Cafe guarantees
that users who liked a user’s favorite song did listen toit. This insight could also be beneficial in designingother music listening systems or services.
6. RELATED WORK
6.1 Music Listening Systems for Group of Users
Music listening systems for a single user were reviewed byGoto and Dannenberg [21] and Knees et al. [22]. In contrast, systems for a group of users can be classified into
two types. The first type aims to enable users to listen
to music at the same time. Most studies on this type assume that users get together in person at a public spacesuch as a fitness center [7], a party [10], a bar [9], or aroom [8]. In MusicFX [7] and Flytrap [8], the system reads
users’ musical preferences from each user’s device, andsongs stored in the system are played by taking those preferences into account, while in Jukola [9], PartyVote [10],and WePlay [12], users nominate songs to be played, likea jukebox. In the second type of group listening system,
users share songs with other users. Sharing music withothers is an important activity to expand listeners’ horizons [4]. Studies on this type do not assume that users
listen to a song at the same time. Push!Music [4] andtunA [3] are mobile music players that let users share songs
via Wi-Fi with others who are nearby. The user studies on
those systems showed that users are comfortable sharing
their favorite songs with others whether they are friends or
strangers. It has also been reported that users share songs
mainly because they want to recommend songs that others would like, disseminate their favorite songs, talk aboutshared songs with others, and so on.
Some applications designed for listening to music together have also been released (e.g., Group Session by
702

Spotify [23] and JQBX [24]). In these applications, anyuser can let other users listen to her favorite songs by acting like a DJ. Users can also communicate with each othervia a text chat system while listening to songs.
Our study is different from the above studies and services in that we introduce the two architectures for reaction
visualization and song selection from users’ favorite songs.
In most of the above cases, because users’ reactions are notvisualized or are visualized only when chatting with text
messages, it is difficult for users to feel social connection
with each other. On the other hand, because the first architecture on Kiite Cafe visualizes four kinds of reactions,users can more strongly feel that they are enjoying musicwith others. In addition, existing systems require users toactively nominate or share songs or act like a DJ, but someusers may hesitate to do that, especially if there is a large
audience. In contrast, the second architecture on Kiite Cafe
enables a user’s favorite songs to be automatically played.
This lets any user share her favorite songs with other users
and see the moment when they start liking those songs.
6.2 Group Recommendation Algorithms
Various song recommendation methods for a single user
have been proposed [25–32]. One of the biggest differences between the methods for a single user and those fora group of users is that the latter methods need to take multiple users’ preferences into account. A general approachis to aggregate each user’s preferences by, for example,
merging recommendation results generated for each user
according to voting strategies [14, 33]. However, such an
approach cannot always reflect minority preferences.
To solve this problem, a concept of fairness has
been recently introduced into group recommendation algorithms [34–38]. The basic idea of fairness is that a list ofitems recommended to a group is fair when each user in thegroup can find at least one item in the list that she finds satisfying. In the context of music recommendation, existingstudies have only considered the fairness for users as audiences. On the other hand, fairness for users as curators aswell as audiences is achieved by Kiite Cafe because of thesecond architecture in which each user’s favorite songs are
fairly selected and played as described in section 2.2.2. In
particular, the “highly recommended list” plays an important role in achieving fairness as curators. When a user’sfavorite and/or recommended song can be listened to withother users, the user is satisfied from audience and curator
viewpoints.
7. CONCLUSION
In this paper, we described Kiite Cafe, a web service thatenables users to communicate while listening to the same
music online. Kiite Cafe is characterized by two proposedarchitectures for visualizing each user’s reactions and selecting played songs from users’ favorite songs. Our experimental results quantitatively showed three effects provided by the proposed architectures. We believe that thesearchitectures are also useful for different types of music
interfaces, including a three-dimensional interface whereuser avatars could listen to the same music in a virtual reality (VR) venue.

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

8. ACKNOWLEDGMENTS
We thank Crypton Future Media, INC. for developing Kiite with us and Nico Nico Douga for initially tacitly (later
explicitly) encouraging us to work on this research. We
also would like to extend our appreciation to users of Kiite and Nico Nico Douga, creators of VOCALOID songs,
creators of popularity rankings of VOCALOID songs, andall people who have created, supported, and enjoyed the
VOCALOID culture. This work was supported in part by
JST ACCEL Grant Number JPMJAC1602, JST CREST
Grant Number JPMJCR20D4, and JSPS KAKENHI Grant
Number 20K19934, Japan.
9. REFERENCES[1] K. Sedgman, “Coughing and clapping: investigatingaudience experience,” Cultural Trends, vol. 24, no. 4,
pp. 324–326, 2015.
[2] S. C. Brown and D. Knox, “Why go to pop concerts?
The motivations behind live music attendance,” Musicae Scientiae, vol. 21, no. 3, pp. 233–249, 2017.
[3] A. Bassoli, J. Moore, S. Agamanolis, and H. C. Group,“tunA: Local music sharing with handheld Wi-Fi devices,” in Proceedings of the 5th Wireless World Conference 2004, ser. WWC ’04, 2004.
[4] M. Håkansson, M. Rost, and L. E. Holmquist, “Gifts
from friends and strangers: A study of mobile music
sharing,” in Proceedings of the 10th European Conference on Computer-Supported Cooperative Work, ser.
ECSCW ’07, 2007, pp. 311–330.
[5] M. Håkansson, M. Rost, M. Jacobsson, and L. E.
Holmquist, “Facilitating mobile music sharing and social interaction with Push!Music,” in Proceedings ofthe 40th Annual Hawaii International Conference on
System Sciences, ser. HICSS ’07, 2007, pp. 87–96.
[6] H. Kenmochi and H. Ohshita, “VOCALOID - commercial singing synthesizer based on sample concatenation,” in Proceedings of the 8th Annual Conference of
the International Speech Communication Association,
ser. INTERSPEECH ’07, 2007, pp. 4009–4010.
[7] J. F. McCarthy and T. D. Anagnost, “MusicFX: An
arbiter of group preferences for computer supported
collaborative workouts,” in Proceedings of the 1998
ACM Conference on Computer Supported Cooperative
Work, ser. CSCW ’98, 1998, pp. 363–372.

[10] D. Sprague, F. Wu, and M. Tory, “Music selection using the PartyVote democratic jukebox,” in Proceedingsof the Working Conference on Advanced Visual Interfaces, ser. AVI ’08, 2008, pp. 433–436.
[11] G. Popescu and P. Pu, “What’s the best music you
have? Designing music recommendation for group enjoyment in GroupFun,” in Proceedings of the CHI ’12
Extended Abstracts on Human Factors in Computing
Systems, ser. CHI EA ’12, 2012, pp. 1673–1678.
[12] F. Vieira and N. Andrade, “Evaluating conflict management mechanisms for online social jukeboxes,” inProceedings of the 16th International Society for Music Information Retrieval Conference, ser. ISMIR ’15,
2015, pp. 190–196.
[13] M. Goto, “Active music listening interfaces based on
signal processing,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal
Processing, ser. ICASSP ’07, 2007, pp. IV–1441–IV–
1444.
[14] M. Kompan and M. Bielikova, “Group recommendations: Survey and perspectives,” Computing and Informatics, vol. 33, no. 2, pp. 446–476, 2014.
[15] E. Pariser, The filter bubble: What the Internet is hidingfrom you. Penguin Press, 2011.
[16] M. Taramigkou, E. Bothos, K. Christidis, D. Apostolou, and G. Mentzas, “Escape the bubble: Guided
exploration of music preferences for serendipity andnovelty,” in Proceedings of the 7th ACM Conference
on Recommender Systems, ser. RecSys ’13, 2013, pp.
335–338.
[17] Y. C. Zhang, D. O. Séaghdha, D. Quercia, and T. Jambor, “Auralist: Introducing serendipity into music recommendation,” in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining,ser. WSDM ’12, 2012, pp. 13–22.
[18] J. Fuller, L. Hubener, Y. Kim, and J. H. Lee, “Elucidating user behavior in music services through persona
and gender,” in Proceedings of the 17th International
Society for Music Information Retrieval Conference,ser. ISMIR ’16, 2016, pp. 626–632.

[8] A. Crossen, J. Budzik, and K. J. Hammond, “Flytrap:
Intelligent group music recommendation,” in Proceedings of the 7th International Conference on Intelligent
User Interfaces, ser. IUI ’02, 2002, pp. 184–185.

[19] C.-N. Ziegler, S. M. McNee, J. A. Konstan, and
G. Lausen, “Improving recommendation lists throughtopic diversification,” in Proceedings of the 14th International Conference on World Wide Web, ser. WWW’05, 2005, pp. 22–32.

[9] K. O’Hara, M. Lipson, M. Jansen, A. Unger, H. Jeffries, and P. Macer, “Jukola: Democratic music choicein a public space,” in Proceedings of the 5th Conference on Designing Interactive Systems: Processes,
Practices, Methods, and Techniques, ser. DIS ’04,
2004, pp. 145–154.

[20] J. Cramer, H.-H. Wu, J. Salamon, and J. P. Bello,“Look, listen, and learn more: Design choices fordeep audio embeddings,” in Proceedings of the 2019
IEEE International Conference on Acoustics, Speechand Signal Processing, ser. ICASSP ’19, 2019, pp.
3852–3856.
703

Proceedings of the 22nd ISMIR Conference, Online, November 7-12, 2021

[21] M. Goto and R. B. Dannenberg, “Music interfaces
based on automatic music signal analysis: New ways
to create and listen to music,” IEEE Signal Processing
Magazine, vol. 36, no. 1, pp. 74–81, 2019.
[22] P. Knees, M. Schedl, and M. Goto, “Intelligent user interfaces for music discovery,” Transactions of the International Society for Music Information Retrieval,vol. 3, no. 1, pp. 165–179, 2020.
[23] “Group session - Spotify,” https://support.spotify.com/
us/article/group-session/.
[24] “JQBX - Listen Together. DJ Online. Discover New
Music.” https://www.jqbx.fm/.
[25] K. Yoshii, M. Goto, K. Komatani, T. Ogata, and H. G.
Okuno, “Hybrid collaborative and content-based music
recommendation using probabilistic model with latent
user preferences,” in Proceedings of the 7th International Conference on Music Information Retrieval, ser.
ISMIR ’06, 2006, pp. 296–301.
[26] M. Tiemann, S. Pauws, and F. Vignoli, “Ensemble
learning for hybrid music recommendation,” in Proceedings of the 8th International Conference on Music
Information Retrieval, ser. ISMIR ’07, 2007, pp. 179–
180.
[27] K. Yoshii and M. Goto, “Continuous pLSI and smoothing techniques for hybrid music recommendation,” inProceedings of the 10th International Society for Music Information Retrieval Conference, ser. ISMIR ’09,
2009, pp. 339–344.
[28] Z. Xing, X. Wang, and Y. Wang, “Enhancing collaborative filtering music recommendation by balancing exploration and exploitation,” in Proceedings of the 15th
International Society for Music Information Retrieval
Conference, ser. ISMIR ’14, 2014, pp. 445–450.
[29] A. Vall, M. Skowron, P. Knees, and M. Schedl, “Improving music recommendations with a weighted factorization of the tagging activity,” in Proceedings of the
16th International Society for Music Information Retrieval Conference, ser. ISMIR ’15, 2015, pp. 65–71.
[30] D. Liang, M. Zhan, and D. P. W. Ellis, “Content-awarecollaborative music recommendation using pre-trainedneural networks,” in Proceedings of the 16th International Society for Music Information Retrieval Conference, ser. ISMIR ’15, 2015, pp. 295–301.
[31] R. S. Oliveira, C. Nóbrega, L. B. Marinho, and N. Andrade, “A multiobjective music recommendation approach for aspect-based diversification,” in Proceedings of the 18th International Society for Music Information Retrieval Conference, ser. ISMIR ’17, 2017,
pp. 414–420.
704[32] O. Gouvert, T. Oberlin, and C. Févotte, “Matrix cofactorization for cold-start recommendation,” in Proceedings of the 19th International Society for Music Information Retrieval Conference, ser. ISMIR ’18, 2018,
pp. 792–798.
[33] L. Baltrunas, T. Makcinskas, and F. Ricci, “Group recommendations with rank aggregation and collaborative
filtering,” in Proceedings of the Fourth ACM Conference on Recommender Systems, ser. RecSys ’10, 2010,
pp. 119–126.
[34] S. Qi, N. Mamoulis, E. Pitoura, and P. Tsaparas, “Recommending packages to groups,” in Proceedings of theIEEE 16th International Conference on Data Mining,ser. ICDM ’16, 2016, pp. 449–458.
[35] D. Serbos, S. Qi, N. Mamoulis, E. Pitoura, and
P. Tsaparas, “Fairness in package-to-group recommendations,” in Proceedings of the 26th International Conference on World Wide Web, ser. WWW ’17, 2017, pp.
371–379.
[36] L. Xiao, Z. Min, Z. Yongfeng, G. Zhaoquan, L. Yiqun,
and M. Shaoping, “Fairness-aware group recommendation with pareto-efficiency,” in Proceedings of theEleventh ACM Conference on Recommender Systems,
ser. RecSys ’17, 2017, pp. 107–115.
[37] D. Sacharidis, “Top-N group recommendations with
fairness,” in Proceedings of the 34th ACM/SIGAPP
Symposium on Applied Computing, ser. SAC ’19, 2019,
pp. 1663–1670.
[38] M. Stratigi, J. Nummenmaa, E. Pitoura, and K. Stefanidis, “Fair sequential group recommendations,” in Proceedings of the 35th Annual ACM Symposium on Applied Computing, ser. SAC ’20, 2020, pp. 1443–1452.