MUSICSUN: A NEW APPROACH TO ARTIST RECOMMENDATION
Elias Pampalk and Masataka Goto
National Institute of Advanced Industrial Science and Technology (AIST)
IT, AIST, 1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
ABSTRACTMusicSun is a graphical user interface to discover artists.
Artists are recommended based on one or more artists selected by the user. The recommendations are computed by
combining 3 different aspects of similarity. The users canchange the impact of each of these aspects. In addition
words are displayed which describe the artists selected by
the user. The user can select one of these words to focus
the search on a specific direction.
In this paper we present the techniques used to compute the recommendations and the graphical user interface. Furthermore, we present the results of an evaluation
with 33 users. We asked them, for example, to judge theusefulness of the different interface components and thequality of the recommendations.
1 INTRODUCTION
Popular music recommendation services include Amazon’s
personalized recommendation lists, personalized Internet
radio (e.g. Last.fm and Pandora), and services that makemusic blogs more accessible (e.g. the Hype Machine). 1
In contrast to most existing recommendation services MusicSun uses neither collaborative filtering nor manually annotated data. Instead we use techniques to automaticallyextract information from audio and web pages.
MusicSun is basically a query-by-example interface. A
user selects one or more artists and is given a list of similar artists. In addition, MusicSun offers the users several
options to modify the recommendations. In particular, theusers can (1) choose which vocabulary the interface uses
to describe artists, (2) select a word from a list of words
summarizing the query which they consider most relevantto their search, and (3) choose which aspects of similarity they are most interested in (the options are: audiobased sound similarity, web-based sociocultural similarity, or similarity with respect to the selected word).
Related work in terms of discovering music includes
a number of very different approaches. One example isthe query-by-example interface (for songs) presented in[1] where an “aha” slider can be used to filter results fromthe same genre and obtain more interesting recommendations. Related work also includes work on visualizing
music collections, enabling the user to easily browse anddiscover new music, e.g., [5, 7, 8, 9, 10, 12, 15, 16]. In
terms of discovering music a notable approach is the work
1 http://last.fm, http://pandora.com, http://hypem.com

c 2007 Austrian Computer Society (OCG).

presented in [3] which efficiently combines web services
to give high quality music recommendations. MusicSun
is closely related to the MusicRainbow user interface [14]with respect to the techniques used and the focus on discovering new artists. However, MusicSun gives the users
more options to focus their search and also requires themto make more choices.
2 TECHNIQUESThe MusicSun interface is generated using audio (tracks)
and the associated artist names as input. Using the artist
names we crawl the web (using Google), and parse theretrieved pages using 4 different vocabularies. Using thisdata extracted from the web we compute two of the three
aspects of similarity we use and summarize individual artists
and groups of artists with words. Using the tracks and audio similarity techniques we compute the third similarity
aspect. In this section we briefly describe these techniquesand how we combine them. Finally, we list some example
recommendations.
2.1 Web-Crawling, Vocabularies, and Summarization
To extract “community information” from the web we use
the approach suggested in [18]. For each artist we query
Google using the artist’s name and the terms “music” and“review” as constraints. We retrieve the top 50 ranked
pages per artist and parse them for any words they contain.
We parse the retrieved pages using 4 manually compiled vocabularies containing words suitable to describe
music (see Table 1). The vocabularies are based on ourprevious work [14] where we used 3 vocabularies which
implied a hierarchy of concepts.
For each artist we count how often words from the vocabularies occur on the retrieved web pages and computethe tf idf weight for each word (see [13] for a more detailed description of the specific implementation we use).
Using these weights we compute similarities as describedin the next section. In addition we use this information to
select words to summarize each artist. We apply the summarization technique suggested in [6]. The basic idea is
to select words that not only occur frequently on the respective web pages, but also are suitable to distinguish thegiven set of artists from all other artists (for further details
see [13]).
There are mainly two limitations to this approach. Firstof all, we assume that artist names are unique identifiers.
However, many artist have ambiguous names. Furthermore, the approach only works for artists which are mentioned on a larger number of web pages.

Vocabulary
Genre/styles
Instruments/types
Moods/adjectives
Countries/regions

ExamplesWordsrock, alternative rap, sunshine pop 255
jazz guitar, female, orchestra
167
smooth, angry, contemporary452
Afro-Cuban, Nashville, European
93
total number of words: 967

Table 1. Description of the 4 vocabularies used.
2.2 Similarity
MusicSun’s recommendations are based on combining 3
similarity aspects. By default these are weighted equally.
However, their weight can be adjusted using sliders. The
first aspect is sound similarity which we compute by analyzing audio contents. The second aspect is socioculturalsimilarity which is computed by analyzing web pages. (Thesociocultural similarity is high if the artist names occur on
the same web pages, or if the same words are used on
pages they occur on.) Third, we compute the similarity
with respect to the word the user has selected to focus thesearch in a specific direction.
Audio-based similarity: The artist similarity is computed
as suggested in [14] which is based on computing the similarity of tracks as described in [11]. To compute the distance between a set of artists (the user’s query) and artist X(a possible candidate for a recommendation), we computethe average distance of X to all artists in the set.
Web-based similarity: As described above we computethe tf idf weighting. The distance between length normalized tf idf vectors is computed using the Euclidean norm.
To compute the distance between a set of artists and artist
X, we compute the average distance of X to all artists inthe set.
Word-based similarity (“sun ray”): One of the main features of the user interface is the option to focus the searchby selecting one of 9 words that summarize the query
artists. For example, when searching for artists similar
to ABBA the user can choose to focus the search, for example, on “pop” or “Swedish”. (One of the 9 options is always randomly chosen by default.) The similarity with respect to the selected word is computed based on the tf idf
weighting. For a given word, the most relevant artist is
the one with the highest tf idf weighting. (Thus the wordbased similarity is independent of the query artists.)
Combined similarity: First, given the query we computethe similarity of every recommendation candidate according to each of the three similarity aspects. Second, we
aggregate the similarity ranks by computing the weighted
average of the rank of each artist. Alternatively, this could
be done by first normalizing the computed similarities andby combining them linearly (see e.g. [2, 12]). The main
advantage of using the ranks is that no normalization isneeded.

Madonna → Pop
1 Britney Spears
2 Lisa Stansfield
3 George Michael
4 Whitney Houston
5 Maria Carey

Madonna → Singing
1 Whitney Houston
2 Lisa Stansfield
3 Maria Carey
4 Britney Spears
5 Macy Gray

Eminem → Controversial
1 Death Row
2 Black Eyed Peas
3 The Streets
4 Dr. Dre
5 Ice-T

Gilberto Gil → Political
1 Caetano Veloso
2 Chico Buarque
3 Caetano Veloso &Gilberto Gil
4 David Byrne
5 Jorge Ben

Table 2. MusicSun example recommendations.
name). The similarity weights were set to their default
values (all weights equal). The top 5 recommendations
are shown. For example, Jorge Ben is the fifth recommendation when using Gilberto Gil as query and setting
the focus on “political”. 2 Changing the focus (e.g. in
the case of Madonna from “pop” to “singing”) does notcompletely change the results because the audio and webbased part of the similarity computation (2/3 of the weightin the default settings) still produce the same results.
3 USER INTERFACE
The components of the MusicSun interface are shown in
Figure 1. A demonstration video is available online. 3 Theuser starts by entering an artist name in the search box.
When the user activates the search box a list slides in fromthe left. After finding an artist the user drags it into the sun(a circle in the center of the screen), the search box slides
away, and the main elements of the interface slide backinto position and are displayed as shown in the Figure 1.
As soon as one or more artists are located in the sun
the rays are labeled with words to summarize the query
artists. The triangular shape of each ray encodes the following information with respect to the word it represents:
If the side of the triangle facing the sun is longer, then therespective word describes the artists better. If the lengthof the ray is longer, then there are more artists in the collection which can also be described using the respective
word. One of the rays is randomly preselected by the system. The user can select a different ray by clicking on it.
Once a ray is selected, it spins itself into the rightmost position, indicating that it is currently being used to modify
the recommendations.
The user can listen to artists by clicking on their names.
A second click plays the next song from the same artist.
From each song only a 20 second excerpt is played which
is selected by the RefraiD chorus detection function [4].
The currently playing song is displayed in the upper left
area together with simple playback controls.
The user can drag artists from the recommendation list
into the sun, or drag them to the area surrounding the sun

2.3 Recommendation Examples
Table 2 shows some example recommendations. For eachof the 4 examples one of the 9 words summarizing theartist is selected (and shown to the right of the artist’s

2 Jorge Ben is often described as less political than all his contemporaries. This is an example where a negation loses its meaning when
focusing only on individual words.
3 http://pampalk.at/musicsun/

1 - Search box to enter (partial) artist names
via the keyboard. A search window
slides in from the left when activated.
2 - Basic audio playback controls (stop &skip song). Playback starts by clicking
on an artist.
3 - Sliders controlling the weights on eachsimilarity aspect.
4 - On/off switches for the vocabularies. In
the screenshot all switches are on.
5 - Mouse over help text is display here.
This information is particularly
important for (3) and (4).
6 - Query artists are placed inside the sun.
7 - The user can choose one of 9 rays. In
the screenshot “rap” is selected.
8 - Storage area: the users can place artists
here which they want to remember orkeep out of the recommendation list.
9 - List of recommendations
10 - History buttons (undo/redo actions)
11 - Previous/next page of recommendations

Figure 1. Screenshot of the MusicSun user interface with 50 Cent, Outkast, and Eminem selected as query.
(“storage area”). This storage area can be used, for example, to keep track of previously found artists. Artists canalso be dragged back into the recommendation list.
The two small circles displayed next to each artist in
the recommendation list encode information about the number of pieces used for the audio analysis (first circle), andthe number of web pages found (second circle). An empty
circle indicates that the system did not have enough data,thus the recommendation is probably flawed. A half empty
circle means that the results are probably questionable. A
full circle means that the amount of data used is about average. In case a Google search for an artists yielded an
extremely high number of pages the circle is shown in a
blueish color. This might indicate either a very famous
artist, or an ambiguous name.
In the recommendation list each artist is described withsome words. These words are computed in the same way
as the rays. More relevant words are located on the left.
If the user changes the vocabularies, then the rays as wellas these artist summaries are recomputed to contain only
words belonging to respective vocabularies. If the userchanges the slider settings the recommendation list is instantly recomputed.
4 EVALUATION AND RESULTS
To evaluate the MusicSun interface we asked 33 volunteers to try out the interface. 16 males and 17 females
participated with an average age of 28.7 years (standard
deviation = 8.4). All participants used computers on adaily basis. 27 participants had previously never used atabled PC. 15 had musical training. 24 stated that they did
not spend any time in the last month actively searching for
new music. 10 primarily discovered artists through recommendations from friends. The second most frequently
recommendation source mentioned was radio followed by

Learning to use MusicSun
Discovering artists

Hard
4
2

=
1
0

Easy
28
26

NA
0
5

Quality of the recommendations
Quality of the artist summaries
Fun factor
Interest in future usage

Low
3
4
1
3

=
2
3
1
1

High
27
16
30
29

NA
1
10
1
0

13
9
8
6

4
4
2
1

11
19
20
24

5
1
3
2

Usefulness of optional components:
small circles in recommendation list
similarity sliders
information encoded in ray shapes
choosing vocabularies

Table 3. Evaluation results: For all questions we used a 7point scale. Users who answered by choosing one of the
3 points on the right or the left of the scale are grouped
into one category. The number of users who selected theexact center of the scale is marked with an equal sign. In
addition, users had the option not to answer a question
which is marked with “NA”.
movie soundtracks. All participants had a musical tastethat partially overlapped with the contents of the musiccollection we used in the evaluation.
The participants were asked to use a tablet PC and pen
as input device. The implemented interface was createdbased on a collection of 999 artists from various genres.
While trying out the interface we asked the users questions which are summarized in Table 3. In average theparticipants spent about 20 minutes with the interface. In
the remainder of this section we summarize some of thequalitative findings.
Describing the unknown: For each artist in the recommendation list a few words summarizing the artist are displayed right under the artist’s name. We asked the participants to rate the quality of these summaries. 16 users

said the quality was good. However most users ignored
these summaries when using the interface. 10 users were
not able to answer the question because they never lookedat them. Instead of reading the summaries they searched
for artist names they were familiar with. In the vicinityof known artists they searched for unknown artists. In addition, an important factor in choosing which of the unknown artists to listen to was the interestingness of therespective name.
Usefulness of reliability indicators: The small circles next
to each artist in the recommendation list indicate how reliable the recommendations are. Overall, the users considered this information least useful. Most users did not
notice them until we pointed them out in the interviews.
However, a few users mentioned that they found it useful
to know if a specific artist is very famous (which correlates with the number of web-pages found), and how manypieces per artists were in the database. Only in one case auser noticed that the circles actually served their purpose(to explain a failure in the recommendations). In this particular case the artist’s name was “Chess” and the circle
indicating the number of web pages was blue.
Similarity weight sliders: Most users tried out the sliders briefly and then ignored them. One user found theresults in the recommendation lists better when the focus
was on web-based similarity. Another user said the samefor audio-based similarity. However, generally it was not
obvious to the users what the differences in the rankings
were when focusing on either one of the two.
The word based slider was extensively used by threeusers to focus their search on, for example, Italian music(this was done by increasing the weight of the word similarity slider and selecting the respective ray). However,reliably extracting country information from unstructured
web pages is difficult. For example, a frequent occurrenceof the word Italy could also mean that the artist held aconcert in Italy.
Vocabulary chooser: In contrast to the similarity sliders
the users found the consequences of choosing a different
vocabulary set easy to understand. Some users focused
only on country and region names, others only on genresand styles.
Requested features: A few users asked to be able to selectmore than one ray at once and to be able to select rays
permanently (one of the biggest problems with the interface is that the rays are recomputed when the artists inside
the sun change, sometimes this means that a previously
selected word disappears).
5 CONCLUSIONSWe presented a new approach to artist recommendation
which combines information extracted from web pages
with information extracted from audio. We built a newquery-by-example(s) user interface which allows the user
to control several recommendation parameters. We conducted an evaluation with 33 users. From the feedback
we conclude that there are two main directions for further
improvements of the user interface: finding better ways tolink unknown artists with known artists, and enabling theuser to select more than one word to focus the search on.
Acknowledgments:
This work was supported by CrestMuse, Crest, JST.
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