The Skipping Behavior of Users of Music Streaming Services and its Relation to Musical Structure

The behavior of users of music streaming services is investigated from the point of view of the temporal dimension of individual songs; specifically, the main object of the analysis is the point in time within a song at which users stop listening and start streaming another song ("skip"). The main contribution of this study is the ascertainment of a correlation between the distribution in time of skipping events and the musical structure of songs. It is also shown that such distribution is not only specific to the individual songs, but also independent of the cohort of users and, under stationary conditions, date of observation. Finally, user behavioral data is used to train a predictor of the musical structure of a song solely from its acoustic content; it is shown that the use of such data, available in large quantities to music streaming services, yields significant improvements in accuracy over the customary fashion of training this class of algorithms, in which only smaller amounts of hand-labeled data are available

Parsing consumption preferences of music streaming audiences

As demands for insights on music streaming listeners continue to grow, scientists and industry analysts face the challenge to comprehend a mutated consumption behavior, which demands a renewed approach to listener typologies. This study aims to determine how audience segmentation can be performed in a time-relevant and replicable manner. Thus, it interrogates which parameters best serve as indicators of preferences to ultimately assist in delimiting listener segments. Accordingly, the primary objective of this research is to develop a revised typology that classifies music streaming listeners in the light of the progressive phenomenology of music listening. The hypothesis assumes that this could be solved by positioning listeners – rather than products – at the center of streaming analysis and supplementing sales- with user-centered metrics. The empirical research of this paper was based on grounded theories, enriched by analytical case studies. For this purpose, behavioral and psychological research results were interconnected with market analysis and streaming platform usage data. Analysis of the results demonstrates that a concatenation of multi-dimensional data streams facilitates the derivation of a typology that is applicable to varying audience pools. The findings indicate that for the delimitation of listener types, the motivation, and listening context are essential key constituents. Since these variables demand insights that reach beyond existing metrics, descriptive data points relating to the listening process are subjoined. Ultimately, parameter indexation results in listener profiles that offer novel access points for investigations, which make imperceptible, interdisciplinary correlations tangible. The framework of the typology can be consulted in analytical and creational processes. In this respect, the results of the derived analytical approach contribute to better determine and ultimately satisfy listener preferences.Während die Nachfrage nach Erkenntnissen über Musik-Streaming-Hörer kontinuierlich steigt, stehen Wissenschaftler sowie Industrieanalysten einem geänderten Konsumptions- verhalten gegenüber, das eine überarbeitete Hörertypologie fordert. Die vorliegende Studie erörtert, wie eine Hörersegmentierung auf zeitgemäße und replizierbare Weise umgesetzt werden kann. Demnach beschäftigt sie sich mit der Frage, welche Parameter am besten als Indikatoren für Hörerpräferenzen dienen und wie diese zur Abgrenzung der Publikumsseg- mente beitragen können. Dementsprechend ist es das primäre Ziel dieser Forschung, eine überarbeitete Typologie aufzustellen, die Musik-Streaming-Hörer in Anbetracht der progressiven Erscheinungsform des Musikhörens klassifiziert. Die Hypothese nimmt an, dass dies realisierbar ist, wenn der Hörer – anstelle von Produkten – im Zentrum der Streaming-Analyse steht und absatzzen- trierte durch hörerzentrierte Messungen ergänzt werden. Die empirische Forschung basiert auf systematischen Theorien, untermauert durch analytische Fallbeispiele. Hierfür werden psychologische und verhaltenswissenschaftliche Forschungserkenntnisse mit Marktanalysen und Nutzerdaten von Musikstreaming-Portalen fusioniert. Die Analyse der Ergebnisse verdeutlicht, dass eine Verkettung von multidimensionalen Rohdaten die Erhebung einer Typologie ermöglicht, die auf mehrere Hörergruppen anwend- bar ist. Die Befunde signalisieren, dass die Hörmotivation und der Hörkontext bei der Abgrenzung der Publikumstypen Schlüsselelemente darstellen. Da diese Variablen spezifis- che Kenntnisse fordern, die über vorliegende Kennzahlen hinausgehen, werden deskriptive Datenpunkte über den Hörvorgang ergänzt. Letztlich, resultiert die Indexierung der Pa- rameter in Hörerprofilen, die neue Zugangspunkte für Untersuchungen bieten, die nicht ersichtliche, interdisziplinäre Korrelationen greifbar machen. Das Gerüst der Hörertypologie kann sowohl in Erstellungs- als auch in Analyseprozessen herangezogen werden. Somit tragen die Ergebnisse der entwickelten Analysemethode zum Verständnis und letztlich zur Erfüllung von Hörerpräferenzen bei

Parsing consumption preferences of music streaming audiences

As demands for insights on music streaming listeners continue to grow, scientists and industry analysts face the challenge to comprehend a mutated consumption behavior, which demands a renewed approach to listener typologies. This study aims to determine how audience segmentation can be performed in a time-relevant and replicable manner. Thus, it interrogates which parameters best serve as indicators of preferences to ultimately assist in delimiting listener segments. Accordingly, the primary objective of this research is to develop a revised typology that classifies music streaming listeners in the light of the progressive phenomenology of music listening. The hypothesis assumes that this could be solved by positioning listeners – rather than products – at the center of streaming analysis and supplementing sales- with user-centered metrics. The empirical research of this paper was based on grounded theories, enriched by analytical case studies. For this purpose, behavioral and psychological research results were interconnected with market analysis and streaming platform usage data. Analysis of the results demonstrates that a concatenation of multi-dimensional data streams facilitates the derivation of a typology that is applicable to varying audience pools. The findings indicate that for the delimitation of listener types, the motivation, and listening context are essential key constituents. Since these variables demand insights that reach beyond existing metrics, descriptive data points relating to the listening process are subjoined. Ultimately, parameter indexation results in listener profiles that offer novel access points for investigations, which make imperceptible, interdisciplinary correlations tangible. The framework of the typology can be consulted in analytical and creational processes. In this respect, the results of the derived analytical approach contribute to better determine and ultimately satisfy listener preferences.Während die Nachfrage nach Erkenntnissen über Musik-Streaming-Hörer kontinuierlich steigt, stehen Wissenschaftler sowie Industrieanalysten einem geänderten Konsumptions- verhalten gegenüber, das eine überarbeitete Hörertypologie fordert. Die vorliegende Studie erörtert, wie eine Hörersegmentierung auf zeitgemäße und replizierbare Weise umgesetzt werden kann. Demnach beschäftigt sie sich mit der Frage, welche Parameter am besten als Indikatoren für Hörerpräferenzen dienen und wie diese zur Abgrenzung der Publikumsseg- mente beitragen können. Dementsprechend ist es das primäre Ziel dieser Forschung, eine überarbeitete Typologie aufzustellen, die Musik-Streaming-Hörer in Anbetracht der progressiven Erscheinungsform des Musikhörens klassifiziert. Die Hypothese nimmt an, dass dies realisierbar ist, wenn der Hörer – anstelle von Produkten – im Zentrum der Streaming-Analyse steht und absatzzen- trierte durch hörerzentrierte Messungen ergänzt werden. Die empirische Forschung basiert auf systematischen Theorien, untermauert durch analytische Fallbeispiele. Hierfür werden psychologische und verhaltenswissenschaftliche Forschungserkenntnisse mit Marktanalysen und Nutzerdaten von Musikstreaming-Portalen fusioniert. Die Analyse der Ergebnisse verdeutlicht, dass eine Verkettung von multidimensionalen Rohdaten die Erhebung einer Typologie ermöglicht, die auf mehrere Hörergruppen anwend- bar ist. Die Befunde signalisieren, dass die Hörmotivation und der Hörkontext bei der Abgrenzung der Publikumstypen Schlüsselelemente darstellen. Da diese Variablen spezifis- che Kenntnisse fordern, die über vorliegende Kennzahlen hinausgehen, werden deskriptive Datenpunkte über den Hörvorgang ergänzt. Letztlich, resultiert die Indexierung der Pa- rameter in Hörerprofilen, die neue Zugangspunkte für Untersuchungen bieten, die nicht ersichtliche, interdisziplinäre Korrelationen greifbar machen. Das Gerüst der Hörertypologie kann sowohl in Erstellungs- als auch in Analyseprozessen herangezogen werden. Somit tragen die Ergebnisse der entwickelten Analysemethode zum Verständnis und letztlich zur Erfüllung von Hörerpräferenzen bei

대조학습을 통한 콘텐츠 기반 음악 추천에서의 비선호도 반영

학위논문(석사) -- 서울대학교대학원 : 융합과학기술대학원 지능정보융합학과, 2023. 2. 이교구.Advanced music recommendation systems are being introduced along with the development of machine learning. However, it is essential to design a music recommendation system that can increase user satisfaction by understanding users music tastes, not by the complexity of models. Although several studies related to music recommendation systems exploiting negative preferences have shown performance improvements, there was a lack of explanation on how they led to better recommendations. In this work, we analyze the role of negative preference in users music tastes by comparing music recommendation models with contrastive learning exploiting prefer- ence (CLEP) but with three different training strategies - exploiting preferences of both positive and negative (CLEP-PN), positive only (CLEP-P), and negative only (CLEP- N). We evaluate the effectiveness of the negative preference by validating each system with a small amount of personalized data obtained via survey and further illuminate the possibility of exploiting negative preference in music recommendations. Our experimental results show that CLEP-N outperforms the other two in accuracy and false positive rate. Furthermore, the proposed training strategies produced a consistent tendency regardless of different types of front-end musical feature extractors, proving the stability of the proposed method.머신러닝의 발전과 함께 이를 활용한 다양한 음악 추천 시스템이 도입되고 있 다. 그러나 음악 추천 시스템에 대한 사용자의 만족도를 높이기 위해서는 단순히 복잡하고 성능이 좋은 모델을 적용하는 것이 아닌, 사용자의 음악 취향에 대한 이해 가 반영된 음악 추천 시스템을 설계해야 한다. 비선호도를 활용한 음악 추천 시스템 역시 여러 연구에서 제안되었는데, 비선호도를 반영함으로써 성능이 향상됨을 보였 지만 비선호도를 반영하는 것이 구체적으로 어떻게 더 나은 추천으로 이어졌는지에 대한 설명은 부족했다. 본 연구를 통해 우리는 선호도와 비선호도를 다르게 적용하여 훈련된 대조 학습 모델(Contrastive Learning Exploiting Preference, CLEP)을 비교 분석함으로써 사용 자의 음악 취향에서 비선호도가 어떤 역할을 가지는지에 대해 알아보고자 한다. 본 연구에서 소개하는 모델은 반영하고자 하는 선호도에 따라 다르게 학습되는 세 가 지 모델을 선호도와 비선호도를 모두 반영한 모델(CLEP-PN), 선호도만을 반영한 모델(CLEP-P), 비선호도만을 반영한 모델(CLEP-N)로 나뉜다. 본연구에서제안한각모델의훈련및평가를위해서설문조사를통해개인선호 도가 포함된 소량의 데이터셋을 구축하였다. 구축한 데이터셋에 대해 각 모델들의 평가 결과를 비교하여 음악 취향에서의 비선호도의 특징과 음악 추천 시스템에서 비선호도를 활용할 수 있는 가능성에 대해 추가로 조명한다. 또한, 음악 데이터로부터 특징을 추출하는 과정에서 사전 학습된 서로 다른 세 가지 모델을 이용하였으며, 특징 추출기와 무관하게 일관된 경향성의 결과를 보여 제안 방법의 안정성을 입증 하였다.1 Introduction 6 1.1 Motivation 6 1.2 Research Questions 9 2 Background 11 2.1 Background Theories 11 2.1.1 Recommender Systems 11 2.1.2 Music Recommendation System 14 2.1.3 Contrastive Learning 16 2.2 Related Works 17 2.2.1 Content-based Music Recommendation 17 2.2.2 Recommendation Systems Exploiting Negative Preference 20 3 Methods 22 3.1 Feature Extraction 22 3.1.1 Contrastive Learning of Musical Representations 24 3.1.2 Music Effects Encoder 25 3.1.3 Jukebox 25 3.2 Contrastive Learning Exploiting Preference (CLEP) 26 3.3 Preference Prediction 29 4 Experiments 30 4.1 Experimental Setups 30 4.2 User Preference Dataset 31 4.3 Evaluation 35 4.3.1 Evaluation Metric 35 4.3.2 Experimental Results 37 5 Results and Discussion 43 6 Conclusion 48 6.1 Contribution 48 6.1.1 Novel Approach on Content-Based Music Recommendation 49 6.1.2 Comprehension of Music Preference 51 6.2 Limitation and Future Works 51석

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field

The Future of Digital Music Services in Three Stereotypes; How Focus Groups of End Users See the New Business Models

“I am just a stereotype” sang Terry Hall in 1980. Ariola records took them in and made the band The Specials a world success. How will that process go in 2014? Will they put it on You Tube for free? Do they need a record company? Will they have less or more fans, earn less or more money? Focus group interviews with 90 people between the ages of 15 and 25 were successfully employed to create 20 new business models for the digital music industry. Analysis with grounded theory revealed that a new business model is necessary and three types for future music services to create and capture value from digital music were found: Social focus; Artist focus and Extra Value focus. More than 50% of the research subjects put the emphasis on social functionalities of the music services, while the value network was underestimated. For artists we see opportunities and threats in the business models: on one hand they can use the worldwide niches to earn money, on the other hand the new business models do not seem to reimburse them enough. Finally, value capture is an overall problem that is best solved in the extra value focus business models. A combination of the three types using the best of each of them guides the way to a successful business model of the futur

Why people skip music? On predicting music skips using deep reinforcement learning

Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users' data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and responsible systems. An example of these interactions is skipping behaviour, a signal that can measure users’ satisfaction, dissatisfaction, or lack of interest. In this paper, we study the utility of users' historical data for the task of sequentially predicting users' skipping behaviour. To this end, we adapt DRL for this classification task, followed by a post-hoc explainability (SHAP) and ablation analysis of the input state representation. Experimental results from a real-world music streaming dataset (Spotify) demonstrate the effectiveness of our approach in this task by outperforming state-of-the-art models. A comprehensive analysis of our approach and of users’ historical data reveals a temporal data leakage problem in the dataset. Our findings indicate that, overall, users' behaviour features are the most discriminative in how our proposed DRL model predicts music skips. Content and contextual features have a lesser effect. This suggests that a limited amount of user data should be collected and leveraged to predict skipping behaviour

Why people skip music? On predicting music skips using deep reinforcement learning

Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users' data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and responsible systems. An example of these interactions is skipping behaviour, a signal that can measure users' satisfaction, dissatisfaction, or lack of interest. In this paper, we study the utility of users' historical data for the task of sequentially predicting users' skipping behaviour. To this end, we adapt DRL for this classification task, followed by a post-hoc explainability (SHAP) and ablation analysis of the input state representation. Experimental results from a real-world music streaming dataset (Spotify) demonstrate the effectiveness of our approach in this task by outperforming state-of-the-art models. A comprehensive analysis of our approach and of users' historical data reveals a temporal data leakage problem in the dataset. Our findings indicate that, overall, users' behaviour features are the most discriminative in how our proposed DRL model predicts music skips. Content and contextual features have a lesser effect. This suggests that a limited amount of user data should be collected and leveraged to predict skipping behaviour

Investigating bias in Music Recommender Systems

Music Recommender Systems (MRS) are software applications that provide personalized music recommendations based on user preferences and listening history. They analyze data to suggest music that aligns with individual tastes, enhancing the music discovery experience. This thesis aims to investigate the influence of record labels across different music recommendation datasets and evaluate their impact on recommender systems. Additionally, it seeks to expand the scope and experimentation of prior research on bias within feedback loops of MRS. To study their effect, the datasets are preprocessed and fed into a multi-stage web crawler that retrieves record label information for individual albums as well as an assignment to a major record company (Universal, Sony, Warner) or independent. This crawler is used to enrich our dataset collection. Based on the additional information, we can show different characteristics and identify particular biases in their user-generated music collections of playlists and listening profiles. Moreover, recommender system experiments are conducted, presenting results of feedback loop simulations, where the stability of record label distribution in longitudinal recommendations are studied. All findings and gathered record label information are made publicly available to the research community.Els Sistemes de Recomanació Musical (MRS) són aplicacions de software que proporcionen recomanacions de música personalitzades basades en les preferències i el històric d'escolta de l'usuari. Analitzen dades per suggerir música que s'ajusti als gustos individuals, millorant així l'experiència de descobriment musical. Aquesta tesi té com a objectiu investigar la influència de les discogràfiques en diferents conjunts de dades de recomanació musical i avaluar el seu impacte en els sistemes de recomanació. A més, busca ampliar l'abast i l'experimentació de recerques prèvies sobre biaixos en els bucles de retroalimentació dels MRS. Per estudiar el seu efecte, els conjunts de dades es pre-processen i s'insereixen a un rastrejador web de diverses etapes que recopila informació sobre les discogràfiques dels àlbums individuals, així com la seva classificació en una discogràfica principal (Universal, Sony, Warner) o independent. Aquest rastrejador s'utilitza per enriquir la nostra col·lecció de dades. Basant-nos en la informació addicional, podem mostrar diferents característiques i identificar biaixos particulars en les col·leccions de música generades pels usuaris, com ara llistes de reproducció i perfils d'escolta. A més, es fan experiments en un entorn simulat de recomanacions, presentant els primers resultats de la simulació de bucles de retroalimentació on s'estudia l'estabilitat de la distribució de segells discogràfics en recomanacions longitudinals. Totes les troballes i la informació recopilada de segells discogràfics es posa a la disposició del públic per a la comunitat investigadora