# Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs

Marta Moscati, Darius Afchar, Markus Schedl, Bruno Sguerra

► To cite this version:

Marta Moscati, Darius Afchar, Markus Schedl, Bruno Sguerra. Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs. 33rd ACM Conference on User Modeling, Adaptation and Personalization (UMAP '25), Jun 2025, New York, NY, United States. pp.63-72, 10.1145/3699682.3728333. hal-05469445

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# Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs

Marta Moscati*

marta.moscati@jku.at
Institute of Computational
Perception, Johannes
Kepler University Linz
Linz, Austria

Darius Afchar

research@deezer.com
Deezer Research
Paris, France

Markus Schedl

markus.schedl@jku.at
Institute of Computational
Perception, Johannes
Kepler University Linz and
Human-centered AI Group,
AI Lab, Linz Institute of
Technology
Linz, Austria

Bruno Sguerra

research@deezer.com
Deezer Research
Paris, France

## Abstract

Humans have the tendency to discover and explore. This natural tendency is reflected in data from streaming platforms as the amount of previously unknown content accessed by users. Additionally, in domains such as that of music streaming there is evidence that recommending novel content improves users' experience with the platform. Therefore, understanding users' discovery patterns, such as the amount to which and the way users access previously unknown content, is a topic of relevance for both the scientific community and the streaming industry, particularly the music one. Previous works studied how music consumption differs for users of different traits and looked at diversity, novelty, and consistency over time of users' music preferences. However, very little is known about how users discover and explore previously unknown music, and how this behavior differs for users of varying discovery needs. In this paper we bridge this gap by analyzing data from a survey answered by users of the major music streaming platform Deezer in combination with their streaming data. We first address questions regarding whether users who declare a higher interest in unfamiliar music listen to more diverse music, have more stable music preferences over time, and explore more music within a same time window, compared to those who declare a lower interest. We then investigate which type of music tracks users choose to listen to when they explore unfamiliar music, identifying clear patterns of popularity and genre representativeness that vary for users of different discovery needs.

Our findings open up possibilities to infer users' interest in unfamiliar music from streaming data as well as possibilities to develop recommender systems that guide users in exploring music in a more natural way.

## CCS Concepts

* Information systems → Personalization; • Applied computing → Psychology; • Human-centered computing → User models.

## ACM Reference Format:

Marta Moscati, Darius Afchar, Markus Schedl, and Bruno Sguerra. 2025. Familiarizing with Music: Discovery Patterns for Different Music Discovery Needs. In *33rd ACM Conference on User Modeling, Adaptation and Personalization (UMAP '25), June 16–19, 2025, New York City, NY, USA*. ACM, New York, NY, USA, 10 pages. https://doi.org/10.1145/3699682.3728333

## 1 Introduction

The question of whether recommendations should reinforce users' established preferences or support users in developing their tastes is a long debated one [44, 53], especially in the domain of music consumption [10]. Research has shown that music listeners tend to like familiar more than unfamiliar music [55], and that users of music streaming platforms tend to listen to music they are familiar with, more often than compared to unfamiliar one [11, 52]. However, there is scientific evidence that humans have the natural tendency to explore [32] and that music liking initially increases as a function of familiarity, but reaches a peak and decreases for higher levels of familiarity with the music [42, 47, 48]. A high-quality music recommendation list should, therefore, find balance between music that is familiar and music that is unfamiliar to the user. Achieving this balance requires a deeper understanding of how music listeners explore new music, which is not only important for the scientific community but also highly relevant from an industry perspective.

In light of these considerations, previous works [14, 16, 26, 28, 30, 31] investigated music exploration behavior by analyzing aspects related to the amount of newly listened content and its diversity defined either at track, artist, or genre level. Often these aspects are analyzed in relation to users' traits. However, these works often lacks granularity regarding several aspects. First, the exploration behavior is not analyzed with respect to the users' self-declared need to discover new music. Although several works considered users' personality traits [6, 14, 17, 19, 25, 31, 36, 43, 46], including openness, these refer to users' general personality and not to music listening specifically. Focusing more on the musical expertise, several works analyzed music listening behavior in relation to users' musical sophistication [15, 30], typically measured through the Goldsmiths Musical Sophistication Index (Gold-MSI) [39]. This

<figure><img src="image_2.png" /></figure>

<footnote>*Work carried out during internship at Deezer. Contact Author.</footnote>
index, however, also captures aspects that go beyond users' self-declared interest in discovering new music, such as their experience as musicians or the money spent on music-related activities. In addition, the analyses often do not consider how the exploration is carried out over time, i. e., the different discovery patterns. For instance, they neglect aspects related to which tracks a user listens to while familiarizing with a music genre they previously did not listen to.

This work bridges these gaps by analyzing the relationship between users' discovery needs and their discovery patterns. We refer to *discovery* as the act of listening to music that was not listened to before, and to *exploration* as the process of discovering tracks of a previously unknown music genre. We consider users' self-reported interest in unfamiliar music as an explicit manifestation of their discovery needs. We analyze users' discovery patterns in terms of several aspects. First, we look at the amount of newly discovered music, the diversity, and stability of music preferences over time. We then analyze the popularity and genre representativeness of tracks listened *while exploring*. Our aim is to answer the following research questions:

**RQ1** Do users who declare a higher interest in unfamiliar music *actually do* discover a larger amount of music compared to those with lower declared interest?

**RQ2** Previous work showed that users with a higher musical sophistication exhibit more diverse and more stable music preferences over time. Does this also apply specifically to users who declare a higher interest in unfamiliar music?

**RQ3** When users explore music they were not familiar with before, such as a newly discovered genre, do they start with tracks that are more popular or more representative of the genre, compared to other tracks of the same genre? Does this tendency vary over users declaring different levels of interest in unfamiliar music?

To address these questions, we carry out an extensive analysis on data from the major music streaming platform Deezer.<fnref n="1" /> This data consists of records of music listening events from the platform as well as data on users' self-reported interest in listening to unfamiliar music as collected by means of an online survey sent to users via email.

Our findings show that users that declare a higher interest in unfamiliar music are more active in discovering music. They also show that users explore previously unknown music clusters through tracks that are more popular and less representative of genres, compared to other tracks of the explored music. Furthermore, the popularity of listened tracks decreases and the representativeness increases while users keep exploring, and these effects vary for users of different levels of self-reported interest in unfamiliar music. Overall, these results provide valuable insights into modeling music discovery. The uncovered relationship between self-reported interest in unfamiliar music and discovery patterns are valuable for music streaming platforms that want to guide users in discovering new genres in a more personalized way.

The rest of the paper is organized as follows: We review previous work related to ours in Section 2, discussing separately music exploration and users' music preferences in relation to their traits. We

introduce the reader to our terminology and describe our methodology in Section 3. The experimental setup is described in Section 4, which also provides an overview of the survey data and of the streaming data used in our analysis. We present the results in Section 5, addressing each research question in a separate subsection. Finally, in Section 6 we contextualize our observations in relation to those of previous work, discuss the limitations and possible extensions of our work.<fnref n="2" />

# 2 Background and Related Work

## 2.1 Music Exploration

Early works based on radio listening showed that familiar music tends to be liked more than unfamiliar music [55]. More recently, users of music streaming platforms have been shown to stream music they are familiar with more often than unfamiliar one [11, 52]. These evidences would indicate that recommending music the user is already familiar with is profitable for music streaming platforms and have also motivated the development of music Recommender Systems (RSs) that leverage models of human memory from cognitive architectures [38, 42, 51]. However, several works showed that music liking initially increases as a function of “unfamiliarity”, it reaches a peak and then decreases for higher levels of unfamiliarity [47, 48]. Furthermore, recommending novel content has been shown to be a direct source of users’ perceived usefulness and satisfaction with recommendations [9, 41]. The purpose of RSs in online streaming platforms therefore does not restrict to reinforcing users’ preferences by recommending them familiar content [10, 44, 53]. This is the case since users have needs to broaden their tastes, a need particularly emerging in the domain of music [30] which motivated the development of many tools for music discovery [7, 8, 23, 24, 26–30, 35, 40, 45, 50]. Additionally, motivated by psychological models of curiosity and exploratory behavior [2–5], several researchers proposed psychology-informed RSs that define for each user an ideal level of unfamiliarity and provide recommendations that match the ideal unfamiliarity level [18, 37, 57]. In order to uncover patterns in the exploratory behavior of users of music streaming platforms, several works [28, 30] analyzed the consistency of music preferences in terms of repeated consumption at either artist, genre, or track level, and at different time scales.

There is, however, a lack of insight on *how* users explore a music genre, after they are exposed to it for the first time. In our work we therefore take a different perspective: We aim at analyzing which tracks users listen to when exploring newly discovered music. We analyze this under different perspectives, such as popularity and genre representativeness of the music tracks, and as a function of the distance in time from the first exposure to similar music.

## 2.2 Music Preference and User Traits

The question of which individuals' traits are associated with specific music preferences or listening behaviors is a long-debated one. Previous studies explored factors related to age [6, 13, 19], gender [19, 33], cognitive style [20], cultural [12, 16, 49], and personality traits [6, 14, 17, 19, 21, 25, 31, 36, 43, 46]. Taking an approach based solely on the streaming data, Villermet et al. [54] also showed that

<footnote n="1">¹https://www.deezer.com/en/</footnote>

<footnote n="2">²The code linked to this work is available at https://github.com/hcai-mms/familiarizing_withMusic, the dataset is linked in the repository.</footnote>
the diversity of music consumption is affected by users' behavioral patterns in the way they choose to access music (e. g., through algorithmic recommendations, editorial playlists, personal playlists, ...). Closer to our work, several works focused on listeners' musical sophistication measured with the Gold-MSI [39]. For instance, Ferwerda and Tkalčič [15] showed that users' musical sophistication correlates with the diversity in music consumption. To further understand music preferences and their development for users of different musical sophistication, Liang and Willemsen carried out several studies on the listening behavior of Spotify users [26, 28–30]. More specifically, the authors analyzed users' musical sophistication in relation to their choice of how far the explored genre should be from their favorite genre, and to the desired level of personalization in the genre exploration. The authors showed that the selection behavior differs for users with different levels of active engagement according to the Gold-MSI, pointing out how users with a higher active engagement tend to explore genres that are closer to their favorite one. These results indicate that different levels of musical sophistication lead to different choices on which music to explore. However, the criterion for the choice of genre (i. e., the distance from the user's favorite genre) is not a strong indication that the user was not exposed to the genre before, or that they were not familiar with it; for instance, we could expect that users are already familiar with genres that are close to their favorite one. Additionally, Liang and Willemsen's works provide insight into how users explore genres focusing on the desired level of personalization. Finally, the active engagement of the Gold-MSI is considered as an aggregated score, combining several aspects, such as the time and money invested in music-related activities.

Our work is complementary to the ones described above. In particular, we analyze users' discoveries for music *they did not stream before* and therefore are less familiar with. Furthermore, we analyze their discovery *patterns*, i. e., how they explore music, by also looking at the popularity and genre representativeness of tracks they choose to listen to while exploring new music. Finally, we focus on a single aspect of the active engagement, which we believe to be most related to their discovery *needs*: the self-reported interest in unfamiliar music as measured by the Gold-MSI.

# 3 Methodology

In this section we describe the terminology and methodology used in our work. The terminology is also summarized in Table 1.

## 3.1 Users' Interest in Unfamiliar Music

We denote users with $u$. Users replied to the question from the Gold-MSI questionnaire “*I'm intrigued by musical styles I'm not familiar with and want to find out more.*” on a 1 (fully disagree) to 5 (fully agree) Likert scale as part of a survey sent via email.<fnref n="3" /> We refer to user $u$'s reply as self-reported Interest in Unfamiliar Music (IUM$^u$). We use IUM$^u$ as a measure of $u$'s discovery needs.

## 3.2 Tracks' Cluster Labels

We denote music tracks with $m$. We identify similar tracks based on their co-occurrence in user-generated playlists and assign similar

tracks the same label $c$. We use *editorial* playlists to validate the meaning of labels $c$ in terms of music genres and *not* for the definition of the labels.<fnref n="4" /> To define labels, we apply $k$-means clustering to vector representations of the user-generated playlist data of music tracks. We use as representations for clustering the embeddings computed by decomposing the mutual-information matrix representing song co-occurrences in Deezer user-generated playlists, decomposed with singular value decomposition (SVD). We use $\ell^2$ as norm for clustering.

We validate the cluster definitions with information from editorial playlists<fnref n="5" /> created by music experts at Deezer.<fnref n="6" /> More specifically, we want to check that different cluster labels $c$ reflect different musical genres, and that tracks closer to cluster centroids are more representative of music genres. For the genres {Pop, Rock, Classical, Electronic},<fnref n="7" /> we select the editorial playlists named “*<genre> Essentials*”. We label each editorial playlist with the cluster labels $c$'s of its tracks, and analyze the similarity between editorial playlists in terms of Jaccard index computed over cluster labels. Labels that are not representative of music genres result in clusters evenly spread across editorial playlists and high Jaccard similarity. In contrast, labels that accurately represent genres lead to lower similarity between playlists. We select the number of clusters $n_{clusters}$ that ensures cluster homogeneity, measured both as the Within-Cluster Sum of Squares (WCSS), and as low Jaccard similarity of the editorial playlists.<fnref n="8" /> Furthermore, we label with $D_m$ the distance of track $m$ from the centroid of the cluster it belongs to. For each cluster $c$, we then compute the average distance $\bar{D}_c$ over tracks in $c$, including tracks that are and tracks that are not in the editorial playlists. $\bar{D}_c$ represents the distance from cluster centroid “expected” for tracks of cluster $c$. For each track in $c$, we compute the relative deviation in distance from the cluster centroid as $\delta D_m = (D_m - \bar{D}_c)/\bar{D}_c$. We then average $\delta D_m$ for tracks $m$ in the editorial playlists. By doing so, an average over tracks in the editorial playlists of $\delta D_m = -100\%$ indicates that these tracks are always located at the centroid, while an average of $\delta D_m = 0\%$ indicates that the average over tracks in editorial playlists is equal to the average over all tracks in the cluster.

We find<fnref n="9" /> that $\delta D_m < 0$, which indicates that tracks in editorial playlists are closer to the cluster centroids compared to tracks that are not in these playlists. Since editorial playlists are curated to reflect a given music genre for multiple users, this finding allows us to consider the deviation in the relative distance from cluster

<footnote n="3">³The original Gold-MSI questionnaire is based on a 7-point Likert scale. The scale was converted to a 5-point one to simplify the form of the questionnaire.</footnote>

<footnote n="4">⁴Other works rely on genre labels extracted from user-generated tags. We excluded the use of user-generated tags to infer music genres since these are often noisy and prone to vandalism, fraud, or strategies intended to misclassify artists' genres.</footnote>

<footnote n="5">⁵Deezer editorial playlists are created by experts of one or more music genres employed at Deezer also for the task of identifying tracks that they believe are most representative of the genre they are expert in. To our knowledge, their choices are not influenced or biased by economic incentives related to the promotion of specific content.</footnote>

<footnote n="6">⁶Notice that the embeddings used for clustering are based on a larger set of user-generated playlists, excluding playlists generated with RSs. The smaller set consisting of editorial playlists is only used to investigate the meaning of the cluster labels.</footnote>

<footnote n="7">⁷We selected Pop, Rock, and Electronic since these are the three genres with the highest number of recordings according to data provided by Discogs (https://mtg.github.io/acousticbrainz-genre-dataset/data_stats/). We chose to extend this with classical music since it provides a genre that is often complementary to more commercial ones.</footnote>

<footnote n="8">⁸We do not control for cluster size since clusters are reflective of music genres and since music genres come with different numbers of tracks.</footnote>

<footnote n="9">⁹We refer the reader to Section 4 for more details.</footnote>
centroids $\delta D_m$ as a measure of the “representativeness” of track $m$ of the music genre of the cluster $c$ it belongs to.

## 3.3 Newly Discovered Music Clusters

We say that user $u$ discovered track $m$ if they listened to it for the first time, and that user $u$ discovered cluster $c$ if they listened for the first time to a track labeled with $c$. We refer to user's $u$ exploration of cluster $c$ as the time-ordered list of first tracks that $u$ listens from cluster $c$. To identify newly discovered music tracks and clusters, we first define the long-term preferences of each user as the tracks and clusters they already listened from the timestamp $t_0$ of their first interaction to a timestamp $t_1$ corresponding to a time-window $[t_0, t_1]$ of six months of listening. We select a six-month window as this is also used by Mehrotra et al. [34] to define discoveries as tracks that a user has not listened before. For each user $u$, we measure the ratio of known tracks $n_c^u$ that belong to cluster $c$, $p_c^u = n_c^u / \sum_c n_c^u$; this ratio varies with time, as users discover new tracks. We therefore label this with $p_c^u(t)$ and define $u$'s long-term preferences $p_c^u(t_1) \in \mathbb{R}^{n_{\text{clusters}}}$ as the vector of ratios $p_c^u(t_1)$ of known tracks per cluster $c$ at the end of the six-months window $[t_0, t_1]$. For the remaining listening events, i. e., those happening at $t > t_1$, we define the time of discovery of a new cluster $c$ as the timestamp of first listen of a track from cluster $c$, i. e., the timestamp $t^*$ such that $p_c^u(t) = 0$ for $t < t^*$ and $p_c^u(t) > 0$ for $t \ge t^*$. We count the number $N^u$ of distinct new clusters discovered in the one-week window $[t_1, t_2]$ following the initial six-months. To measure how the amount of discovered music varies with $IUM^u$, we first compute Pearson's correlation coefficient$^{10}$ between $N^u$ and $IUM^u$ over all users. Furthermore, we apply bootstrapping to estimate the mean and standard error of $N^u$ for groups of users of different $IUM^u$ and plot these as a function of $IUM^u$. We take 1,000 samples of sample size 100, sampling with replacement.

## 3.4 Diversity and Stability of Music Preferences

We quantify the diversity and time evolution of music preferences in terms of relative amounts of known tracks per clusters, $p_c^u(t)$. We measure diversity [12, 15, 30] as Shannon's entropy$^{11}$ over clusters, $\text{div}^u(t) = -\sum_c p_c^u(t) \log p_c^u(t)$. We measure the time evolution of music preferences as time derivative$^{12}$ of $\text{div}^u(t)$ and of $\|p^u(t)\|_2$, i. e., the $\ell^2$ norm of the vector $p^u(t) \in \mathbb{R}^{n_{\text{clusters}}}$ representing the relative amount of known tracks per clusters. We name the resulting quantities Stability Preference with Entropy ($\text{SP}_E^u$) and Stability Preference with Norm ($\text{SP}_N^u$), respectively.

Both $\text{SP}_E^u$ and $\text{SP}_N^u$ measure how $u$'s diversity in music preferences evolves in time. Positive values of $\text{SP}_E^u$ indicate that $u$'s diversity is increasing (increase in $\text{div}^u$ over time), while positive values of $\text{SP}_N^u$ that $u$'s diversity is decreasing (towards $p^u(t)$ being directed along one $p_c^u(t)$ axis only).$^{13}$

The six-months window of $u$'s initial listening events $[t_0, t_1]$ is first used to determine the baseline of $u$'s preferences, $p^u(t_1) \in \mathbb{R}^{n_{\text{clusters}}}$, i. e.,

the vector of ratios $p_c^u(t_1)$ of known tracks per cluster $c$ at time $t_1$. We then average $\text{SP}_E^u$ and $\text{SP}_N^u$ over the listening events of $u$ for $t \ge t_1$ to keep into account artifacts of the beginning of the streaming data.

To measure how the diversity and stability of music preferences vary with $IUM^u$, we compute Pearson's correlation coefficient$^{14}$ between $\text{div}^u(t_1)$ (the diversity after six months of listening), $\text{SP}_E^u$, and $\text{SP}_N^u$ with $IUM^u$ over all users. We then apply bootstrapping to estimate the mean and standard error of $\text{div}^u(t_1)$, of $\text{SP}_E^u$, and of $\text{SP}_N^u$ within each group of users $IUM^u \in [1, \dots, 5]$ and plot these as a function of $IUM^u$. We take 1,000 samples of sample size 100, sampling with replacement.

## 3.5 Time Exploration of Music Clusters

We first explore how popularity and cluster representativeness evolve over the first tracks listened when a music cluster is explored, and over all users, i. e., irrespective of $IUM^u$. Building upon the analysis on the editorial playlists described in Section 3.2, we measure the cluster representativeness of track $m$ in $c$ as $\delta D_m$, i. e., the deviation in its $\ell^2$ distance from the centroid of cluster $c$ relative to the average distance of tracks in $c$. We define the popularity $P_m$ of track $m$ as the logarithm of the total number of its listening events; in a similar way to $\delta D_m$, we define $\delta P_m = (P_m - \bar{P}_c)/\bar{P}_c$ as the relative deviation of $m$'s popularity from the average popularity of tracks of the same cluster $c$. We do so to keep into account that different clusters might have different distributions of popularity and distance from cluster centroid.

When user $u$ discovers cluster $c$, we assign each track $m$ a value according to its Rank in the Exploration ($\text{ExpR}_m^u$) of $c$. By doing so, $\text{ExpR}_m^u = 1$ for the first track $u$ listened from $c$, $\text{ExpR}_m^u = 2$ for the second track, and so on.$^{15}$ To measure how $\delta P_m$ and $\delta D_m$ vary with $\text{ExpR}_m^u$ we compute Pearson's correlation between $\text{ExpR}_m^u$ and both $\delta P_m$ and $\delta D_m$. To display the evolution of $\delta D_m$ graphically, we apply principal component analysis (PCA) to the SVD embeddings of all tracks. Then, for each value of $\text{ExpR}_m^u$, the distribution of distances from the cluster centroid of the first two principal components is approximated with kernel density estimation (KDE) using a Gaussian kernel. The KDE is used to plot the isolines of the density for the values 0.2, 0.4, and 0.6%. These isolines therefore give an overview of the genre representativeness in the exploration of clusters, and across all users. For each value of $\text{ExpR}_m^u$, we apply bootstrapping to estimate the mean and standard error of the distribution of $\delta P_m$ of tracks of rank $\text{ExpR}_m^u$. We take 1,000 samples of sample size 100, sampling with replacement. Finally, to analyze the exploration of music clusters for different levels of $IUM^u$, we perform two multivariate linear regressions, one of $\delta P_m$ and one of $\delta D_m$, as a function of both the time-rank $\text{ExpR}_m^u$ of the explored track, and of $IUM^u$.

# 4 Experimental Setup

We perform our analyses on a set of users of the music streaming platform Deezer. The dataset was collected within the project

<footnote>$^{10}$Using Spearman's rank correlation leads to similar results (see repository).</footnote>

<footnote>$^{11}$We refer the reader to the repository for an analysis in terms of Rao-Stirling diversity [56], leading to analogous results. We expect similar results also when using GS-score as diversity measure, due to the high correlation of the two metrics [1].</footnote>

<footnote>$^{12}$We approximate the derivative with the difference quotient.</footnote>

<footnote>$^{13}$Notice that $\|p^u(t)\|_2^2$ corresponds to Simpson's index, whose inverse is also often used as diversity measure [15].</footnote>

<footnote>$^{14}$Using Spearman's rank correlation leads to similar results (see repository).</footnote>

<footnote>$^{15}$We stop at rank $\text{ExpR}_m^u = 10$, for which the analyzed trends stabilize and refer the reader to the repository for higher ranks.</footnote>
<table><caption>Table 1: Summary of the terminology. *u* indicates a user, *m* a music track, *t* a timestamp.</caption><thead><tr><th>Variable</th><th>Description</th></tr></thead><tbody><tr><td>IUM<sup>u</sup></td><td>u's interest in unfamiliar music i. e., reply to "I'm in-<br>trigued by [...] more." on a 1 (fully disagree) to 5 (fully<br>agree) Likert scale.</td></tr><tr><td>N<sup>u</sup></td><td>Number of distinct clusters discovered by u in a one-<br>week window.</td></tr><tr><td>δP<sub>m</sub></td><td>Popularity of m relative to the average popularity of<br>its cluster c. δP<sub>m</sub> = 0% means that m has popularity<br>equal to c's average, δP<sub>m</sub> > 0% that m is more popular<br>than c's average.</td></tr><tr><td>δD<sub>m</sub></td><td>Distance of m from the centroid of its cluster c and<br>relative to the average distance of tracks in c. δD<sub>m</sub> =<br>0% if m's distance from the centroid is equal to the<br>average in c, δD<sub>m</sub> > 0% if m is further away from the<br>centroid and less representative of c.</td></tr><tr><td>p<sup>u</sup><sub>c</sub>(t)</td><td>Ratio of tracks known to u that are in c at time t.</td></tr><tr><td>p<sup>u</sup>(t)</td><td>u's preferences at time t expressed as vector of ratios<br>of known tracks p<sup>u</sup><sub>c</sub>(t).</td></tr><tr><td>div<sup>u</sup>(t)</td><td>Diversity of u's music preferences at time t measured<br>as Shannon's entropy over components of p<sup>u</sup>(t).</td></tr><tr><td>SP<sup>u</sup><sub>E</sub></td><td>Stability of u's music preferences measured as time<br>derivative of div<sup>u</sup>(t), averaged over time.</td></tr><tr><td>SP<sup>u</sup><sub>N</sub></td><td>Stability of u's music preferences measured as time<br>derivative of ||p<sup>u</sup>(t)||<sub>2</sub>, averaged over time.</td></tr><tr><td>ExpR<sup>u</sup><sub>m</sub></td><td>m's rank in u's exploration of cluster c. ExpR<sup>u</sup><sub>m</sub> =<br>1 for the first track listened by u in c.</td></tr></tbody></table>

RECORDS,<fnref n="16" /> funded by the French National Agency for Research, whose aim is to study practices on music streaming platforms. In particular, we consider users that took part in a survey, sent to users via email, regarding their music listening practices, musical tastes and socio-demographic variables. The survey was developed to evaluate users' musical sophistication based on the Gold-MSI questionnaire [39],<fnref n="17" /> to which users replied on a 5-point Likert scale. We assign *u* a value of IUM<sup>u</sup> equal to their reply to "I'm intrigued by musical styles I'm not familiar with and want to find out more". In addition, the dataset provides the listening events of these users from 01/01/2022 till 31/12/2022. We only consider listening events that lasted longer than 30 seconds, a threshold often applied in music streaming for remuneration purposes, and in music recommendation to identify positive feedback [22, 51]; to ensure that user *u* "familiarized" with track *m*, we disregard user-track pairs (*u*, *m*) for which *u* listened to *m* only once in this period. For each user-track pair IUM<sup>u</sup> we keep the timestamp *t* of first interaction as indication of when *u* discovered *m* (and the respective cluster *c*, if no track from *c* was listened before). We restrict to users that replied to all questions from the Gold-MSI and that provided demographic information. We apply 10-core filtering and only restrict to users that listened to at least 10 distinct tracks, and tracks that were listened by at least 10 distinct users. This leads to 4,070 users, 72,555 tracks, and to 28,678,935 user-track interactions, corresponding to 3,425,564 unique user-track pairs (*u*, *m*), as summarized in Table 3. The users constitute of 2,207 male, 1,863 female; the collected replies IUM<sup>u</sup> had an average of 3.2 and

<figure><img src="image_3.png" /><figcaption>Figure 1: Histogram of IUM<sup>u</sup>, i. e., of replies to the question "I'm intrigued by musical styles I'm not familiar with and want to find out more.". IUM<sup>u</sup> = 1: fully disagree; IUM<sup>u</sup> = 5: fully agree.</figcaption></figure>

a standard deviation of 1.0. Figure 1 shows the histogram of IUM<sup>u</sup> while Table 2 reports, for each gender, the number of users who responded with each allowed value of IUM<sup>u</sup>.

We vary the number *n*<sub>clusters</sub> of clusters in {8, 16, 32, 64, 128, 256}. To ensure cluster homogeneity, we select *n*<sub>clusters</sub> as the smallest value above the elbow in the WCSS plot, and for which the Jaccard index computed over cluster labels between any pair of editorial playlists is below 1/3, indicating that for any pair of editorial playlists, the overlapping cluster labels are less than 1/3 of the labels appearing in any of the two playlists. This leads to *n*<sub>clusters</sub> = 128.

## 5 Results

Before moving to the results on RQ1, 2, and 3, we provide more insight on the validation of cluster labels *c* and on *δD*<sub>*m*</sub> in relation to cluster representativeness.

The column "# Editorial Tracks" in Table 4 shows the number of distinct tracks that are present in the selected editorial playlists,<fnref n="18" /> while the column "# Editorial Tracks in the Streaming Data" shows the number of tracks appearing both in the selected editorial playlists and in the streaming data used in our analysis. The last column shows the average of *δD*<sub>*m*</sub> over tracks appearing both in the selected editorial playlists and in the streaming data used in our analysis. We see that on average tracks that appear in editorial playlists are closer to cluster centroids than other tracks, since for editorial tracks *δD*<sub>*m*</sub> < 0. This motivates our use of *δD*<sub>*m*</sub> as measure of track representativeness of the genre of a cluster.

### 5.1 RQ 1: Newly Discovered Music Clusters

Figure 2 shows the number *N*<sup>u</sup> of explored music clusters in [*t*<sub>1</sub>, *t*<sub>2</sub>], i. e., within a one-week period after the initial six months [*t*<sub>0</sub>, *t*<sub>1</sub>]. The central dots and error bars represent the mean and standard error of *N*<sup>u</sup> for different values of IUM<sup>u</sup>, computed as described in Section 3.3. Overall the plot shows that users of higher IUM<sup>u</sup> tend

<footnote n="16">¹⁶RECORDS web page: https://records.huma-num.fr/</footnote>

<footnote n="17">¹⁷Gold-MSI web page: https://shiny.gold-msi.org/gmsi_t LEVEL/</footnote>

<footnote n="18">¹⁸This table refers to the validation of cluster labels *c* and representativeness of centroids. It does not describe the full set of tracks of the streaming data used in our analyses. For a summary of the streaming data, we refer the reader to Table 3.</footnote>
<table><caption>Table 2: Number of users by gender (m, f), and by IUM$^u$, i. e., reply to the question “I'm intrigued by musical styles I'm not familiar with and want to find out more.”. IUM$^u$ = 1: fully disagree; IUM$^u$ = 5: fully agree. Percentages are expressed relative to participants of the same gender.</caption><thead><tr><th></th><th>IUM<sup>u</sup> = 1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><th>m</th><td>135 (6%)</td><td>393 (18%)</td><td>794 (36%)</td><td>683 (31%)</td><td>202 (9%)</td></tr><tr><th>f</th><td>98 (5%)</td><td>368 (20%)</td><td>717 (39%)</td><td>512 (27%)</td><td>168 (9%)</td></tr></tbody></table>

<table><caption>Table 3: Characteristics of the streaming data. "# Interactions" consider listening events individually, counting relistening of a same track as separate events. "# Distinct (u, m) pairs" aggregates listening events of a same track from a same user, considering relistening as a single pair.</caption><thead><tr><th># Users u</th><th># Tracks m</th><th># Interactions</th><th># Distinct (u, m) pairs</th></tr></thead><tbody><tr><td>4,070</td><td>72,555</td><td>28,678,935</td><td>3,425,564</td></tr></tbody></table>

<table><caption>Table 4: Number of tracks of the editorial playlists for Classical, Electronic, Pop, and Rock, and of tracks of the streaming data that also appear in the editorial playlists. Last column: δD<sub>m</sub>, i. e., distance from cluster centroid relative to the mean distance of tracks of the same cluster, averaged over tracks of the editorial playlist.</caption><thead><tr><th>Editorial Playlist</th><th># Editorial Tracks</th><th># Editorial Tracks in the Streaming Data</th><th>Average δD<sub>m</sub></th></tr></thead><tbody><tr><td>Classical Essentials</td><td>23</td><td>23</td><td>-31%</td></tr><tr><td>Electronic Essentials</td><td>27</td><td>18</td><td>-7%</td></tr><tr><td>Pop Essentials</td><td>81</td><td>55</td><td>-15%</td></tr><tr><td>Rock Essentials</td><td>70</td><td>65</td><td>-21%</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>Figure 2: Average number N$^u$ of clusters explored in [t<sub>1</sub>, t<sub>2</sub>] (one week after initial six months) for different values of IUM$^u$. Central dots and error bars represent mean and standard errors estimated with bootstrapping (see Section 3.3).</figcaption></figure>

to explore, within a same time-window, more music clusters than users of lower IUM$^u$. The most remarkable difference is between users of IUM$^u$ = 1 and IUM$^u$ = 4, the latter exploring on average 1.5 more music clusters than the former, in one week (N$^u$ ≈ 8.5 vs. N$^u$ ≈ 7). Overall, this trend is confirmed by the positive, albeit low, correlation coefficient (ρ = 0.06, p = 0.00161) computed over all users between N$^u$ and IUM$^u$, indicating a weak correlation.

## 5.2 RQ 2: Diversity and Stability of Music Preferences

The diversity div$^u(t_1)$ of music consumption computed as entropy over listened clusters at the end of the initial six-months period, is displayed in Figure 3a. Higher values of div$^u(t_1)$ indicate music preferences more equally distributed over different clusters, and lower values indicate music preferences more peaked over few clusters. The figure clearly shows an increasing trend, indicating that users who declare a higher interest in unfamiliar music IUM$^u$ also have higher div$^u(t_1)$, and hence more diverse music tastes. This dependency is confirmed by the positive correlation coefficient

computed over all users (ρ = 0.19, p < 10<sup>-5</sup>) between IUM$^u$ and div$^u(t_1)$. It is in line with the results from previous works [15, 30], which grouped users according to their active engagement values of the Gold-MSI, a value to which the self-reported interest in unfamiliar music IUM$^u$ also contributes. Notice that this insight differs from the one described in Section 5.1: While Figure 2 indicates that users with higher interest in unfamiliar music explore more clusters, Figure 3a indicates that they have more evenly spread interests over clusters.

Figures 3b and 3c show SP<sub>E</sub><sup>u</sup> and SP<sub>N</sub><sup>u</sup> averaged over the listening events of the users for t > t<sub>1</sub>, i. e., the time derivative of diversity and of the ℓ<sup>2</sup> norm of p<sup>u</sup>(t) after the initial six-months period. Although SP<sub>E</sub><sup>u</sup> marginally increases for increasing values of IUM<sup>u</sup>, we do not observe any significant difference among users of different IUM<sup>u</sup> in neither SP<sub>E</sub><sup>u</sup> nor SP<sub>N</sub><sup>u</sup>. This is confirmed by the low correlation coefficients (ρ = 0.02 for SP<sub>E</sub><sup>u</sup>(t) and ρ = 0.008 for SP<sub>N</sub><sup>u</sup>(t)) and indicates that, although users of higher IUM<sup>u</sup> explore more music clusters and have more equally spread music tastes, the rate of change of the relative amount of music listened per cluster is roughly the same for all user groups.

## 5.3 RQ 3: Time Exploration of Music Clusters

Finally, we look at how users explore newly discovered music clusters. In particular, we look at cluster representativeness and popularity of tracks newly listened after the cluster discovery, and relative to tracks of the same cluster. Figure 4a shows, for the first two principal components, the KDE-approximated isolines of the distribution of distances from cluster centroid. As described in Section 3.5, these distributions are computed over all users, and for tracks of different rank ExpR<sub>m</sub><sup>u</sup> in the exploration of a newly discovered cluster, indicated by the different colors. Darker lines correspond to tracks explored later than lighter ones. To de-clutter the plot, we chose to display the isolines for ExpR<sub>m</sub><sup>u</sup> = 1, 5, 10 only, i. e., the first, the fifth, and the tenth listened tracks. We see that tracks that are listened later (isolines for ExpR<sub>m</sub><sup>u</sup> = 5 in comparison to ExpR<sub>m</sub><sup>u</sup> = 1, and of ExpR<sub>m</sub><sup>u</sup> = 10 in comparison to ExpR<sub>m</sub><sup>u</sup> = 5 and ExpR<sub>m</sub><sup>u</sup> = 1) are on average closer to the cluster centroids. As
<figure><img src="image_5.png" /><figcaption>(a) Diversity $div^u(t_1)$, i. e., Shannon entropy over music clusters after six months listening, for different users' IUM$^u$.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>(b) Music preference stability as $SP_E^u$, i. e., time-derivative of $div^u(t)$, for different users' IUM$^u$.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>(c) Music preference stability as $SP_N^u$, i. e., time-derivative of $\|p^u(t)\|_2$, for different users' IUM$^u$.</figcaption></figure>

<caption>Figure 3: Diversity and stability of music preferences for different declared interest in unfamiliar music IUM$^u$. Central dots and error bars represent the mean and standard errors estimated with bootstrapping.</caption>

revealed by the analysis on tracks in editorial playlists, cluster centroids seem to be more representative of the genres for the playlists investigated. Figure 4a therefore indicates that after discovering a music cluster (i. e., after the first listen), users tend to listen to tracks that are more representative of the genre of the cluster.

Figure 4b shows the popularity of tracks newly listened after the cluster discovery and for different $ExpR_m^u \in [1, 2, ..., 10]$. The popularity $\delta P_m$ is given as a relative number in relation to the average popularity of tracks of the same cluster. We observe that $\delta P_m > 0$ for all values of $ExpR_m^u$. This indicates that in the initial phases of cluster exploration, i. e., for the first ten tracks, users listen to tracks that are more popular than the average of the same cluster. Furthermore, we observe that $\delta P_m$ decreases as $ExpR_m^u$ increases; more specifically, the plot indicates that users start exploring a

<figure><img src="image_8.png" /><figcaption>(a) Isolines for 0.2, 0.4, and 0.6% of the distribution of distances from cluster centroids, for the first two principal components and for $ExpR_m^u = 1, 5, 10$. The distributions are approximated with KDE, as described in Section 3.5.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>(b) Popularity $\delta P_m$ over track time-rank $ExpR_m^u$ in the cluster exploration. Central dots and error bars represent mean and standard errors estimated with bootstrapping (see Section 3.5).</figcaption></figure>

<caption>Figure 4: Clusters exploration displayed as a function of the time-rank $ExpR_m^u$ of tracks listened to in a newly discovered cluster. These plots are computed over all users.</caption>

cluster from tracks that, on average, have a popularity ~ 26% higher than the average of the cluster, and later listen to tracks that are less popular than the starting one. The difference in $\delta P_m$ between two consecutively listened tracks decreases as $ExpR_m^u$ increases. This shows that users drift to less popular tracks with a faster rate at the beginning of the exploration of a cluster. The decrease in popularity and increase in cluster representativeness (i. e., decrease in $\delta D_m$) during cluster exploration are confirmed by the negative values of the correlation coefficients of $\delta P_m$ and $\delta D_m$ with $ExpR_m^u$. ($\rho = -0.11$ for $\delta P_m$ and $\rho = -0.15$ for $\delta D_m$. In both cases $p < 10^{-5}$.)

The analyses summarized by Figure 4 are carried out over all users, irrespective of their self-reported interest in unfamiliar music. To investigate the correlation between self-reported interest
<table><caption>Table 5: Results of the multivariate linear regression of popularity δP<sub>m</sub> and distance from cluster centroid δD<sub>m</sub> on ExpR<sup>u</sup><sub>m</sub> and IUM<sup>u</sup>. ***p < 0.001; **p < 0.01; *p < 0.05.</caption><thead><tr><th rowspan="2"></th><th colspan="2">δP<sub>m</sub></th><th colspan="2">δD<sub>m</sub></th></tr><tr><th>Parameter</th><th>Std. Err.</th><th>Parameter</th><th>Std. Err.</th></tr></thead><tbody><tr><td>Intercept</td><td>0.3144***</td><td>0.0045</td><td>0.1076***</td><td>0.0068</td></tr><tr><td>ExpR<sup>u</sup><sub>m</sub> Slope</td><td>-0.0150***</td><td>0.0007</td><td>-0.0359***</td><td>0.0011</td></tr><tr><td>IUM<sup>u</sup> Slope</td><td>-0.0105***</td><td>0.0013</td><td>0.0053**</td><td>0.0019</td></tr></tbody></table>

in unfamiliar music IUM<sup>u</sup> and cluster discovery patterns we perform two separate multivariate linear regressions, one of cluster representativeness measured as δD<sub>m</sub>, and one of popularity δP<sub>m</sub>. Both regressions are performed on user interest in unfamiliar music IUM<sup>u</sup> and track time-rank ExpR<sup>u</sup><sub>m</sub> in the cluster exploration, considered as independent variables. The results of the regressions are reported in Table 5. First of all, the positive values of the intercepts for both δP<sub>m</sub> (0.314, *p* < 0.001) and δD<sub>m</sub> (0.108, *p* < 0.001) again indicate that users start exploring new clusters from tracks that are more popular, and less representative (more distant from the centroid) than the average track in the cluster. The negative slopes for ExpR<sup>u</sup><sub>m</sub> (-0.0150 for popularity δP<sub>m</sub>, -0.0359 for distance from centroid δD<sub>m</sub>, in both cases *p* < 0.001) again indicate that tracks that are listened later in the cluster exploration are less popular, but more representative of the music of the explored cluster. Finally, let us look at the estimated dependence on IUM<sup>u</sup>, i. e., on self-reported interest in unfamiliar music. The negative slope for δP<sub>m</sub> (-0.0105, *p* < 0.001) indicates that users of high levels of IUM<sup>u</sup> tend to explore clusters through tracks that are less popular, compared to users with low IUM<sup>u</sup> levels. In contrast, the positive slope for δD<sub>m</sub> (0.0053, *p* < 0.01) indicates that tracks are less representative (more distant from the centroid) of the clusters for users of higher IUM<sup>u</sup>.

# 6 Conclusions, Limitations, and Future Work

In summary, our work showed how music discovery patterns vary for users of different discovery needs. In particular, we showed that:

**RQ1** Users of higher declared interest in unfamiliar music explore more music compared to those with lower declared interest.

**RQ2** Users of higher declared interest in unfamiliar music exhibit more diverse music preferences than those with lower declared interest. We did not observe any substantial difference in the stability of music preferences over time.

**RQ3** When users explore a new music cluster, they start from tracks that are more popular and less representative of genres, compared to the average track of the same cluster. While exploring, users gradually move to tracks that are less popular, but more representative of the cluster genres. In addition, compared to those of lower declared interest, users of higher interest in unfamiliar music tend to explore music through tracks that are less popular and less representative of genres than the average track in the explored cluster.

There are several limitations and potential extensions to our work. First, the observation that the stability of users' music preferences over time does not vary over levels of interest in unfamiliar music seems to challenge previous findings on the relation between active engagement and stability of music preferences [30]. However, our

analysis focused on interest in unfamiliar music specifically, which is one of the seven aspects summarized by the overall active engagement level. Due to space limits, we did not extend the analysis to the other individual aspects. Future works could focus on the other aspects and identify the origin of the discrepancy. The increasing representativeness in the exploration of a cluster might be an indication that users are transitioning to a newly discovered cluster from a previously known one, a hypothesis that could be tested in the future. The users sample consisted of users who replied to the survey and might therefore not be representative of the population of Deezer users (and of users of music streaming platforms more generally). In addition, our analysis uses a six-month window for the definition of cluster discovery and long-term preferences. Although previous work on music streaming considered similar time-ranges [30, 34], this definition does not capture cases in which a user is familiar with a track but did not stream it for a long time, or in which a user familiarized with the track through other means, e. g., listening to radio stations. The way we modeled exploration behavior considered the rank in the exploration phase, but did not consider aspects related to the time difference between consecutive discovers of a same cluster, such as whether these tracks were explored in a same listening session. We also did not distinguish between *lean-back* and *lean-in* streams, a distinction that might be particularly relevant to uncover exploration behavior. Although the clustering is based on the track co-occurrence in user-generated playlists from Deezer, and although we showed that tracks that are in Deezer editorial playlists are closer to cluster centroids, carrying out the analysis in terms of clusters instead of, e. g., tag-based genres, renders some of the analyses more abstract. Finally, we carried out our analyses only in the music domain and on one dataset. This is due to the absence of other large-scale streaming dataset published together with users' replies to the Gold-MSI questionnaire.

In addition to those mentioned above, extensions to our work could look at whether cluster exploration and repeated consumption of tracks from a same cluster show the same trends of satiation as for repeated consumption of individual music tracks, extensively analyzed in past works [42, 47]. Finally, one could leverage the insight gained in this work to develop RSs that guide users in exploring music by adjusting the levels of popularity and of genre representativeness of recommended tracks. By means of a user study one could further test if adjusting these aspects to the users' self-reported interest in unfamiliar music leads to a higher satisfaction with recommendations for music exploration.

Our work opens up possibilities to infer users' interest in unfamiliar music from streaming data, as well as to develop RSs that guide users in exploring music in a more natural way by considering popularity and genre representativeness of recommendations.

# Acknowledgments

This research was funded in whole or in part by the Austrian Science Fund (FWF) https://doi.org/10.55776/P33526, https://doi.org/10.55776/DFH23, https://doi.org/10.55776/COE12, https://doi.org/10.55776/P36413, and by the French National Agency of Research Grant (ANR) "RECORDS" (ANR-2019-CE38-0013).
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