Listening histories are rich sources of implicit information. Combining listening histories of multiple users allows comparisons in musical taste and discovery of new music, though few existing work specifically addresses this issue. In this paper we present two interactive visualizations which give users a deeper insight into consent and dissent in their listening behaviors, and help them to compare their musical tastes. HisFlocks shows overlaps in genre and artist in certain time periods and LoomFM illustrates sequential listening patterns. Our first feedback on these systems was very promising and we plan to extend our concepts to broader scenarios with a greater number of listening histories.
INTRODUCTION
"What kind of music do you like?" is a common question
while getting to know someone. Learning about musical
taste is appealing and there are various effects of social
bonding surrounding music [
In this paper, we explore different interactive visualization concepts for multiple users’ listening histories and propose two prototypes which facilitate the comparison of multiple users’ musical tastes.
There are some existing visualization tools regarding
timebased data and specifically listening history. Time-based
data is mostly visualized as a temporal flow which indicates
hidden trends over time. ThemeRiver [
In order to gain more insight into the effective use of metadata contributed by the users, we discussed with 13 Last.fm users about the issue of musical taste and listening history.
During the interview, the participants were shown the
Last.fm Explorer visualization result of their own listening history and one of another participant. The concept of visualization of multiple users’ listening histories received positive feedback. It was appreciated that the history can be displayed in three different levels. Participants appreciated that they could control the visualization with interactions such as filtering, stacking and brushing. But their comments revealed that Last.fm Explorer's focus on a single user's listening history leads to some intrinsic issues in need of improvement for visualizing multiple histories: As shown in Figure 1, only a recent part of history can be shown in the limited space, so the two timelines might be misaligned especially when one user did not listen to music at the beginning or end of the dataset. Although information can be displayed on three different levels, the lack of zooming might cause users to lose the current context. It is not possible, for example, to learn an artist's genre by switching from artist to genre view, because the size and placement of items change. The main issue of Last.fm Explorer claimed by the participants, however, is that it is difficult to actually compare user's taste in music: The stacked graphs make the impact of certain artists or tracks hard to read and finding correlations in two graphs can only be performed by a lengthy visual search process. Although existing visualization tools receive positive feedback, most of them target single users and present static, non-interactive images. There is a lack of tools for browsing and comparing multiple users’ listening histories directly instead of tediously clicking through web pages. VISUALIZATION OF MULTIPLE USERS’ LISTENING HISTORIES Based on the analysis of existing tools and results of the interview, we gathered the following design principles for visualizing multiple users’ listening histories within an interactive interface: Time period consistency: Since a listening history is timebased, the visualization should be built with a global timeline. Each listening history should be aligned to facilitate comparisons within the same time period.
Good overview: The visualization should offer a good overview especially for long listening histories, so the user can get a general impression at first glance without continuously switching back and forth.
Managing complexity: Music-related information is complex and has metadata on various levels. The most prominent ones are genre, artist and track. Combined with information about the consumption behavior, such as listening histories, the complexity steadily increases. Therefore it is essential to support the user's task specifically comparison by focusing on key data elements and controls.
Easy to compare: Multiple listening histories should be represented in one interface to facilitate convenient comparison between different users.
Based on these design principles, we developed two different visualizations, Last.fm HisFlocks and LoomFM, aiming at different aspects: HisFlocks gives users an overview of relations in genre and artist in certain time periods, while LoomFM allows a comparison of consumption patterns over time. We will present these two applications here. Last.fm HisFlocks Since the genre and artist level provide a better overview than the track level, we chose genre and artist as the main categories and visualized this general information in our first interface Last.fm HisFlocks (see Figure 2).
In HisFlocks, the entire history is represented as a series of
time frames with all the artists listened to in each time
period being displayed on a genre map, where similar artists
are grouped into clusters, labeled with genre names. By
default, the time interval between each frame is one week,
but this can be adjusted freely. Within a frame, each artist’s
name is color-coded (1 color per compared user) and its
size represents the play count. Shared artists are further
grouped into highlighted sub-clusters. The user can pan and
zoom to navigate within the map. In different frames, the
same artist always appears at the same position. As Figure 2
shows, an overview for all the artists listened to is provided
in the first frame and by dragging the slider, the user can
browse all frames sequentially in which the artists being
listened to in this time period are highlighted and others
fade out. The user can easily see the change of an artist, for
example, by appearance, disappearance and growth of size,
while she or he adjusts the time slider, and thus have an
understanding of how the musical taste changes over time.
In Figure 1, the two users share a lot in metal, which is
scattered in some sub-genres such as heavy metal, gothic metal
and melodic death metal, and thus cannot be derived at first
glance. Therefore, some aggregation of these tags could
improve the understandability of musical similarity on the
genre level. In HisFlocks, all the artists in one frame are
grouped into genre-clusters based on their most
representative genre-tags. This organization is based on a semantic
analysis which determines the structure and position for
each genre and artist. We applied text analysis to create a
semantic clustering of similar tags while excluding
redundancy: After removal of different separators, such as “_”
and “&”, the Porter algorithm [
Sim( A, B) =| A I B | / | A U B |
(1)
With the semantic analysis all the tags will be well
organized: the initial location of each tag is assigned by means of
a 2D projection based on a multidimensional scaling of
cooccurrences (see [
The overview shown in Figure 2 indicates that both users listen to rock and metal a lot, and they do share many artists in these two genres. Other genres they also listen to are punk, industrial and electro. Only the user with the blue color listens to folk, pop and jazz. Although they have many artists in common, they listen to few of them at the exact same time period. For example, in one time period as Figure 3 shows, two users both listened to 3 common artists (Killswitch Engage, Children of Bodom, Dream Theater), but only one (Dream Theater) was exactly listened to by both users in this time period. Besides, only the “red” user listened to “Symphony X”.
LoomFM
HisFlocks focuses on displaying artists on an abstract genre
map while hiding the “sequential”, track-based aspects of
listening behavior: How many songs are consumed in one
session? How often are certain songs listened to? Are there
repeating patterns within this list (such as albums or
predefined playlists) and large gaps (due to vacations etc.)?
To address these issues we designed the complementary
visualization LoomFM (see Figures 4 and 5). A horizontal
timeline displays the temporal scope of two listening
histories. Based on that, songs of one user are displayed above
and of the other below this timeline and symbolized as
small circles (hovering with the mouse above an icon
gives more detailed information about what song it is and
when and by whom it was listened to). The distance of a
song icon to the middle timeline represents the overlap with
the other history: similar songs will be shown with their
titles and displayed very close to the center, while less
similar songs that are only represented by their artists or even
their genres are further away (with a completely unrelated
song at the farthest). The up and down of the song circles
thus represents the general similarity between the two users.
Instances of songs that appear more than once are
connected by yellow arcs within one history as well as between
histories. Similar to Arc Diagrams [
The user can again navigate the displayed song plane by panning and zooming. Zooming out gives an overview of the complete listening histories with the most influential genres and artists still visible.
SUMMARY AND FUTURE WORK Music listening histories and their accompanying metadata are complex information that can bring rich insights into one’s own and other’s behavior. We explore the possibilities to facilitate browsing and comparing multiple users’ listening histories, and present two interactive visualization approaches: To illustrate how the musical taste changes over time, both of the visualizations are time-based. They emphasize certain aspects of the music-related data in order to reduce the complexity and support visual comparison by merging the users' histories into one representative graph. Last.fm HisFlocks focuses on time periods and aggregates musical taste based on genres and artists within these periods. LoomFM is more timeline-centric and highlights sequential aspects of histories, such as repetitions and the differences in taste over time. These visualizations provide users with means to get a deeper understanding for neighboring listeners and also discover new songs. Given that both systems are implemented as first prototypes, some issues need to be improved in upcoming versions. In HisFlocks, although the same artist always appears at the same position, the overall similarity-based placing of clusters and that of artists in each cluster is rather arbitrary. Users cannot anticipate where exactly new artists will appear. A more comprehensible placement would be appealing. LoomFM suffers from a low performance when displaying more than a thousand songs, an issue that comes up with realistically sized collections.
We discussed our prototypes with Last.fm users and received rather positive feedback and valuable suggestions. We plan to add some extra functionalities, such as filters. In the current state only two histories are compared and we plan to examine our concepts with a far greater number and size of listening histories. We hope to gain additional ideas regarding understandability, data reliability and the human perception of musical taste. In our future work, we will extend our concepts to broader scenarios with consumption record, for example with books or photos.
ACKNOWLEDGMENTS This research was funded by the Chinese Scholarship Council (CSC) and the German state of Bavaria. We would like to thank the participants of our study.