=Paper=
{{Paper
|id=Vol-1680/paper8
|storemode=property
|title=A Comparative Analysis of Personality-Based Music Recommender Systems
|pdfUrl=https://ceur-ws.org/Vol-1680/paper8.pdf
|volume=Vol-1680
|authors=Melissa Onori,Alessandro Micarelli,Giuseppe Sansonetti
|dblpUrl=https://dblp.org/rec/conf/recsys/OnoriMS16
}}
==A Comparative Analysis of Personality-Based Music Recommender Systems==
https://ceur-ws.org/Vol-1680/paper8.pdf
A Comparative Analysis of Personality-Based
Music Recommender Systems
Melissa Onori Alessandro Micarelli Giuseppe Sansonetti
Department of Engineering Department of Engineering Department of Engineering
Roma Tre University Roma Tre University Roma Tre University
Via della Vasca Navale, 79 Via della Vasca Navale, 79 Via della Vasca Navale, 79
00146 Rome, Italy 00146 Rome, Italy 00146 Rome, Italy
melissa.onori@gmail.com micarel@dia.uniroma3.it gsansone@dia.uniroma3.it
ABSTRACT reveal how information about a user’s personality can help
This article describes a preliminary study on considering in- infer her music preferences and contribute to a more accu-
formation about the target user’s personality in music rec- rate recommendation process [31]. Therefore, several note-
ommender systems (MRSs). For this purpose, we devised worthy MRSs considering the active user’s personality have
and implemented four MRSs and evaluated them on a sam- been proposed. Among others, Ferwerda and Schedl [12]
ple of real users and real-world datasets. Experimental re- propose an approach where users’ personality and emotional
sults show that MRSs that rely on purely users’ personal- states are implicitly extracted by analyzing their microblogs
ity information are able to provide performance comparable on Twitter. The authors make use of the extraction tech-
with those of a state-of-the-art MRS, even better in terms niques described by Golbeck [14] and Quercia et al. [30],
of the diversity of the suggested items. also trying to combine them for better predictions. Hu and
Pu [18] compare a personality test-based MRS with a classic
rating-based one. The authors point out that users are more
Keywords inclined to results returned from the former. According to
Personality; music recommendation; evaluation Hu and Pu, the active user perceives less effort and less
time to use the personality test-based MRS. They further
1. INTRODUCTION claim that users show a strong intention to use such MRS
again and an unexpected surprise in its results, as they feel
Music plays an important role in entertainment and leisure that the personality-based approach is able to reveal their
of human beings. With the advent of Web 2.0, a huge hidden preferences, thereby improving the recommendation
amount of music content has been made available to millions process. Also Tkalc̆ic̆ et al. [34] show that recommenders
of people around the world. This has provided new oppor- based on Big Five data can outperform rating-based recom-
tunities for researchers working on music information with menders. In [19], Hu and Pu consider again their previous
the aim of creating new services that support navigation, results, exploring the use of personality tests for creating
discovery, sharing, and the development of online communi- psychological profiles of user’s friends as well. They enable
ties among users. Music recommender systems (MRSs) aim the MRS to generate recommendations for users and their
to predict what people like to listen to. A recent research friends too. They also suggest that personality-based MRSs
field in music recommendation explores the possibility of are preferred by no music connoisseurs, which do not know
harnessing information on the target user’s personality in their music preferences in depth.
the recommendation process.
The goal of the research work described in this paper is
to assess the potential benefits of such integration. To this 3. PERSONALITY
end, we implemented and compared with each other different Generally speaking, an individual’s personality can be de-
MRSs, three of them based on users’ personality inferred fined as a combination of characteristics and qualities that
from explicit and implicit feedbacks, and one that does not make up the way she thinks, feels, and behaves in different
consider users’ personality. situations [33]. Personality and emotions shape our every-
day life, having a strong influence on our tastes [32], deci-
2. RELATED WORK sions [29], purchases [6], and general behavior [7]. It has
In the research literature, there exist several works that been shown that people with similar personalities turn out
to have similar preferences [8]. However, giving a more rigor-
ous definition of personality can be challenging, so different
theories have been formulated to specifically make easier the
comprehension of self and others [9]. Each of these theories
differently addresses the problem of representing and charac-
terizing the human personality. We are interested in theories
that would allow us to differentiate people from each other
through measurable traits. The subject of the psychology
EMPIRE 2016, September 16, 2016, Boston, MA, USA. of personality traits is the study of the psychological differ-
Copyright held by the author(s). ences between individuals and relies on empirical research.
Initially, it was studied and defined by Gordon W.Allport [1, the advantage of not having to force the user to answer a
2], which specified 17953 specific traits to describe an indi- significant number of questions. Along this direction, the au-
vidual’s personality. Then, particular effort was devoted to thors developed the Apply Magic Sauce (AMS)1 that allows
the attempt of limiting the number of traits that would oth- for the prediction of users’ personality from the analysis of
erwise be unmanageable. This led to the definition of the their activities on Facebook. Such application, developed at
well-known Big Five Model [11]. After several revisions, the the University of Cambridge Psychometrics Centre, relies on
Big Five factors were finally labeled as follows [24]: over six million social media profiles and determines person-
ality traits through psychometric evaluations, as described
• Extraversion; in [23]. The model is based on the dataset of myPersonality
• Agreeableness; project 2 .
• Conscientiousness;
4. USER STUDY
• Neuroticism; In this section, we describe the dataset, the setup, and the
results of the experimental evaluation.
• Openness (to experience).
In spite of several criticisms (e.g., [21]), such model is widely 4.1 Dataset
adopted in various fields, ranging from Medicine to Business. The experimental tests were performed on the Last.fm 3
From the computer science point of view, personality traits music listening data kindly provided by the researchers of
include a set of human characteristics that can be modeled myPersonality project [22] and Liam McNamara [26]. From
and implemented, for example, in personalized services. Pre- this data, we extracted 1,875 Last.fm users with related
diction of personality traits can be accomplished explicitly information about personality tests and listening histories.
(e.g., by administering personality tests), or implicitly (e.g., The user’s preferences were inferred from the playcount at-
by monitoring the user’s behavior). tribute, which denotes how many times the user listened to
that particular song. The final value is obtained by normal-
3.1 Explicit Acquisition izing it between 1 and 5.
Nowadays, questionnaires are the most popular method
for extracting an individual’s personality. They consist of 4.2 Users
a more or less large number of different questions, which The users who took part in the experimental trials were
are directly related to the granularity of the traits to be de- 65, all of them with an active Facebook account. Their
termined. Nunes et al. [28] show that the number of items characteristics in terms of gender, age, occupation, and ed-
influences the accuracy of measurements of traits. As ex- ucation are illustrated in Tables 1, 2, 3, 4, respectively.
pected, the higher the number of items, the more accurate
the traits extracted. In particular, personality tests based on
the Big Five Model are numerous and varied. A reasonable Table 1: Gender
trade-off between accuracy and ease of use is represented Female Male
by the Big Five Inventory (BFI) [4]. The 44-item BFI has 27 38
been developed to create a brief questionnaire for efficient
and flexible inference of the five factors, without the need to
define more individual facets [21].
Table 2: Age
3.2 Implicit Acquisition
0-18 19-24 25-29 30-35 36-45 46-55 56-65
An individual’s online behavior has long been the subject
2 25 26 2 5 4 1
of many studies in the social sciences [3, 7, 25]. Results in
cognitive psychology show that the general factors of per-
sonality can predict the aspects of the Internet use [25]. In
fact, personality traits can be reflected in users’ actions and
ways of surfing the Web [3, 10, 27]. There are also studies
that investigate the possibility of inferring the user’s person- Table 3: Occupation
ality by user-generated content on social networks such as None Student Employee Professional Housewife
Facebook and Twitter. For instance, Gao et al. derive users’ 6 35 21 2 1
personality traits from their microblogs [13]. Golbeck et al.
identify users’ personality traits by analyzing their Facebook
profiles, including peculiarities of language, business, and
personal information [15]. Moreover, Golbeck et al. [14] and
Table 4: Education
Quercia et al. [30] predict users’ personality from Twitter, Primary Secondary Bachelor Master PhD
by examining their tweet content and observing their char-
6 29 18 10 2
acteristics (e.g., popularity, influential users, etc.). Kosinski
et al. [23] show that likes on Facebook can be used to auto-
matically and accurately predict a set of personal attributes,
1
including personality traits. For instance, the accuracy of http://applymagicsauce.com/
2
prediction of the Openness factor is similar to the accuracy www.mypersonality.org
3
that can be obtained through a classic personality test, with http://www.last.fm/
4.3 Setup precisely, this MRS identifies the most similar users to the
For presenting the user with the suggested playlists we target one within a dataset containing information related
designed a simple interface that allows for a quick and easy to personality and music habits of a group of Last.fm users.
use of the system. Furthermore, we made us of the Spo- The user u’s personality is compared to that of each user v
tify APIs 4 , which offer a preview of 30 seconds of each in the dataset by computing the cosine similarity applied to
song in the playlist. We deemed such time enough for the the Big Five factors, which is defined as follows:
user to understand whether a given song is to her liking or P5 k k
k=1 pu × pv
not. Moreover, listening to the whole playlist is short, thus simp(u, v) = qP qP (1)
5 k )2 5 k )2
avoiding that the user will get bored and stop listening to k=1 (p u k=1 (p v
the recommended songs. In this way, the user will be able
to express a well-founded opinion. where pku expresses the value of the Big Five factor k of the
Each user was required to test all MRSs and evaluate the user u. Based on such values, the system selects the ten
returned playlists. MRSs were proposed in a random order Last.fm users most similar to the user u and generates a
and with the user completely unaware of their details. Rat- playlist from their listening histories.
ings expressed by users in the evaluation phase were related
to novelty, serendipity, diversity, interest, and future use. To 4.4.3 MRS based on Implicit Personality and Neigh-
this end, each user was asked to provide an assessment in bors
relation to the following five statements: The implicit personality acquisition can be carried out by
analyzing the user’s behavior on the Web, especially on so-
1. “I found new songs by artists already known to me.” cial networks. To this end, we used the APIs of the Apply
(novelty) Magic Sauce (AMS) application introduced in Section 3.2.
In order to infer the user’s personality, AMS analyzes how
2. “I found songs by artists that I did not know and, as she assigns likes on Facebook. For such reason, the system
of now, will begin to listen to.” (serendipity) allows users to login via Facebook. In this way, AMS en-
ters the user profile, extracts the required information, and
3. “I found songs by artists of different music genres.”
returns the predicted information, such as age, intelligence,
(diversity)
life satisfaction, interest in specific areas, and her personality
4. “I found the suggested playlist interesting.” (interest) traits. Based on such features, the MRS identifies the most
similar users to the target one within the Last.fm dataset by
5. “I would use this MRS again in the future.” (future computing the similarity function 1. From the information
use) related to the music such users listen to, the MRS builds the
personalized playlist for the active user. However, this MRS
For each of these statements the user could express a numer- has a drawback: it is necessary that the user has inserted a
ical value in a Likert 5-point scale (i.e., 1: strongly disagree, sufficient number of likes in her profile. Otherwise, the AMS
5: strongly agree). In addition, the user could leave a feed- application is not able to predict the user’s personality and,
back as well. as a result, the MRS is not able to deliver any playlist.
4.4 Music Recommender Systems 4.4.4 MRS based on Music Preferences
In this section, we introduce the music recommender sys- This MRS does not exploit information about the user’s
tems (MRSs) developed as part of our research work. personality, and has been realized as a baseline to be used
in the experimental evaluation. The recommender works as
4.4.1 MRS based on Relations between Explicit Per- follows. The user is presented with a screenshot of the im-
sonality and Music Genres ages of ten songs belonging to the Last.fm top track, and
The first MRS acquires information on the target user’s is asked to choose her favorites. Alternatively, the user can
personality explicitly, by administering a personality test. enter the title of some of her favorite songs. After that, the
We chose the 44-item Big Five Inventory test introduced in system leverages the Last.fm APIs to retrieve songs similar
Section 3.1, as its length represents an appropriate trade-off to those chosen by the user and includes them in the sug-
between compilation time and results accuracy. Such test is gested playlist. Even though the actual algorithm underly-
proposed to the target user through a web interface. Once ing the Last.fm recommender is unknown, it is reasonable
the test is completed, the system analyzes the responses and to assume that it mostly relies on collaborative filtering and
computes the Big Five factors. In [8], the relations between tagging activity.
users’ personality types and their preferences in multiple
entertainment domains are investigated. The authors derive 4.5 Results
a set of association rules that connect the Big Five factors Experimental results are shown in Table 5. In the descrip-
with music genres. Based on those rules, this MRS returns tion of the experimental results, the implemented MRSs are
the resulting playlist to the user. denoted as follows:
4.4.2 MRS based on Explicit Personality and Neigh- I: MRS based on relations between explicit personality and
bors music genres;
Even the second MRS relies on the user’s personality ex- II: MRS based on explicit personality and neighbors;
plicitly inferred through the use of the questionnaire. The
recommendation mechanism, however, is different. More IIII: MRS based on implicit personality and neighbors;
4
https://developer.spotify.com/web-api/ IV: MRS based on music preferences.
Table 5: Results in terms of mean and standard deviation of user ratings
MRS # of Users Novelty Serendipity Diversity Interest Future Use
I 65 2.5 - 1.0 2.5 - 0.8 3.0 - 0.9 3.0 - 0.8 3.4 - 0.8
II 65 2.4 - 0.9 2.6 - 0.8 2.8 - 0.8 3.2 - 0.7 3.3 - 0.8
III 43 2.2 - 0.7 2.2 - 0.6 3.2 - 0.9 2.4 - 0.7 2.8 - 0.9
IV 65 2.9 - 0.8 2.4 - 0.9 1.7 - 0.5 3.5 - 0.6 3.5 - 0.6
The reason for the smaller number of users who experienced kindly providing the datasets used in the experimental eval-
the third MRS (i.e., the one based on implicit personality uation.
and neighbors) was that not all testers had a sufficient num-
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