The Structure of Musical Preferences of Youth: Cross-cultural Perspective

The aim of this study was to explore the differences in musical preferences between Slovene and Croatian students. The sample consisted of 369 students from Slovenia and 371 students from Croatia. The results show that there are significant differences in musical preferences between Slovene and Croatian students. Furthermore, differences with regard to gender, age and study program were confirmed

Selecting music for exercise: The music preferences of UK exercisers

There is currently little information regarding the music preferences of exercisers. This absence might have important implications given suggestions that an aversion to a music style and/or piece might constrain the proposed extra-musical benefits. This study aimed to address this. Utilising an online survey, one thousand, one hundred and forty-five UK exercisers were questioned as to their listening preferences whilst exercising. Overall, the results highlighted a predilection for genres such as Pop, Dance/House and Rock. However, Chi-squared analysis revealed some gender differences; for example, the tendency for males to prefer “heavier” music styles. There was also a move towards less mainstream/contemporary music with age for both males and females. The outcomes of this study offer guidance to those tasked with selecting musical accompaniment for exercise within both the practical and research domains

A hybrid recommender system for improving automatic playlist continuation

Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.This work has been partially supported by the Catalan Agency for Management of University and Research Grants (AGAUR) (2017 SGR 574), by the European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program –COMPETE 2020– (POCI-01-0145-FEDER006961), and by the Portuguese Foundation for Science and Technology (FCT) (UID/EEA/50014/2013).Peer ReviewedPostprint (author's final draft

Listener Modeling and Context-aware Music Recommendation Based on Country Archetypes

Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions: First, using state-of-the-art unsupervised learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-a-vis state-of-the-art algorithms that do not exploit this type of context information.Comment: 30 pages, 3 tables, 12 figure

A hybrid approach for item collection recommendations : an application to automatic playlist continuation

Current recommender systems aim mainly to generate accurate item recommendations, without properly evaluating the multiple dimensions of the recommendation problem. However, in many domains, like in music, where items are rarely consumed in isolation, users would rather need a set of items, designed to work well together, while having some cognitive properties as a whole, related to their perception of quality and satisfaction. In this thesis, a hybrid case-based recommendation approach for item collections is proposed. In particular, an application to automatic playlist continuation, addressing similar cognitive concepts, rather than similar users, is presented. Playlists, that are sets of music items designed to be consumed as a sequence, with a specific purpose and within a specific context, are treated as cases. The proposed recommender system is based on a meta-level hybridization. First, Latent Dirichlet Allocation is applied to the set of past playlists, described as distributions over music styles, to identify their underlying concepts. Then, for a started playlist, its semantic characteristics, like its latent concept and the styles of the included items, are inferred, and Case-Based Reasoning is applied to the set of past playlists addressing the same concept, to construct and recommend a relevant playlist continuation. A graph-based item model is used to overcome the semantic gap between songs’ signal-based descriptions and users’ high-level preferences, efficiently capture the playlists’ structures and the similarity of the music items in those. As the proposed method bases its reasoning on previous playlists, it does not require the construction of complex user profiles to generate accurate recommendations. Furthermore, apart from relevance, support to parameters beyond accuracy, like increased coherence or support to diverse items is provided to deliver a more complete user experience. Experiments on real music datasets have revealed improved results, compared to other state of the art techniques, while achieving a “good trade-off” between recommendations’ relevance, diversity and coherence. Finally, although actually focusing on playlist continuations, the designed approach could be easily adapted to serve other recommendation domains with similar characteristics.Los sistemas de recomendación actuales tienen como objetivo principal generar recomendaciones precisas de artículos, sin evaluar propiamente las múltiples dimensiones del problema de recomendación. Sin embargo, en dominios como la música, donde los artículos rara vez se consumen en forma aislada, los usuarios más bien necesitarían recibir recomendaciones de conjuntos de elementos, diseñados para que se complementaran bien juntos, mientras se cubran algunas propiedades cognitivas, relacionadas con su percepción de calidad y satisfacción. En esta tesis, se propone un sistema híbrido de recomendación meta-nivel, que genera recomendaciones de colecciones de artículos. En particular, el sistema se centra en la generación automática de continuaciones de listas de música, tratando conceptos cognitivos similares, en lugar de usuarios similares. Las listas de reproducción son conjuntos de elementos musicales diseñados para ser consumidos en secuencia, con un propósito específico y dentro de un contexto específico. El sistema propuesto primero aplica el método de Latent Dirichlet Allocation a las listas de reproducción, que se describen como distribuciones sobre estilos musicales, para identificar sus conceptos. Cuando se ha iniciado una nueva lista, se deducen sus características semánticas, como su concepto y los estilos de los elementos incluidos en ella. A continuación, el sistema aplica razonamiento basado en casos, utilizando las listas del mismo concepto, para construir y recomendar una continuación relevante. Se utiliza un grafo que modeliza las relaciones de los elementos, para superar el ?salto semántico? existente entre las descripciones de las canciones, normalmente basadas en características sonoras, y las preferencias de los usuarios, expresadas en características de alto nivel. También se utiliza para calcular la similitud de los elementos musicales y para capturar la estructura de las listas de dichos elementos. Como el método propuesto basa su razonamiento en las listas de reproducción y no en usuarios que las construyeron, no se requiere la construcción de perfiles de usuarios complejos para poder generar recomendaciones precisas. Aparte de la relevancia de las recomendaciones, el sistema tiene en cuenta parámetros más allá de la precisión, como mayor coherencia o soporte a la diversidad de los elementos para enriquecer la experiencia del usuario. Los experimentos realizados en bases de datos reales, han revelado mejores resultados, en comparación con las técnicas utilizadas normalmente. Al mismo tiempo, el algoritmo propuesto logra un "buen equilibrio" entre la relevancia, la diversidad y la coherencia de las recomendaciones generadas. Finalmente, aunque la metodología presentada se centra en la recomendación de continuaciones de listas de reproducción musical, el sistema se puede adaptar fácilmente a otros dominios con características similares.Postprint (published version

The relationship between musical sophistication, executive functions and autistic traits

The overarching goal of this thesis is to examine how musical sophistication and/or specific dimension of musical sophistication are related to autistic traits, EF and quality of life in the general population. Chapters 2 and 3 focus on validating the AQ as the AQ was used throughout the studies of the thesis. Chapter 2 investigated whether language influences the response to the AQ among multilingual Malaysians. Specifically, participants’ responses to the AQ in their native language and English were compared. Chapter 3 examined the psychometric properties of an abridged version of the AQ (i.e., AQ-28) in the Dutch and Malaysian general population, and whether the autistic traits as measured by the AQ-28 are comparable between Dutch and Malaysian participants. Chapter 4 investigated if autistic traits would be associated with certain music preferences after controlling for other factors (e.g., age, gender, personality traits and musical ability) that are known to influence music preferences. Chapter 5 explored if listening to preferred music would improve the performance on EF tasks compared to relaxing music and silence and whether autistic traits and EDA are associated with the performance on EF tasks. The relationship between autistic traits, musical sophistication, EF, and quality of life was examined in Chapter 6. The current thesis demonstrates that greater musical sophistication is associated with better EF, and in turn, better quality of life. Active engagement in the form of music listening, however, does not seem to influence EF. Higher autistic traits are associated with poorer quality of life and a reduced preference for Contemporary music. Arousal seems not elevated in response to self-selected music and not associated with EF and autistic traits. Results concerning psychometric properties of AQ, music preference, personality and music listening on cognitive performance do not fully replicate previous findings from the Western contexts

The relationship between musical sophistication, executive functions and autistic traits

The overarching goal of this thesis is to examine how musical sophistication and/or specific dimension of musical sophistication are related to autistic traits, EF and quality of life in the general population. Chapters 2 and 3 focus on validating the AQ as the AQ was used throughout the studies of the thesis. Chapter 2 investigated whether language influences the response to the AQ among multilingual Malaysians. Specifically, participants’ responses to the AQ in their native language and English were compared. Chapter 3 examined the psychometric properties of an abridged version of the AQ (i.e., AQ-28) in the Dutch and Malaysian general population, and whether the autistic traits as measured by the AQ-28 are comparable between Dutch and Malaysian participants. Chapter 4 investigated if autistic traits would be associated with certain music preferences after controlling for other factors (e.g., age, gender, personality traits and musical ability) that are known to influence music preferences. Chapter 5 explored if listening to preferred music would improve the performance on EF tasks compared to relaxing music and silence and whether autistic traits and EDA are associated with the performance on EF tasks. The relationship between autistic traits, musical sophistication, EF, and quality of life was examined in Chapter 6. The current thesis demonstrates that greater musical sophistication is associated with better EF, and in turn, better quality of life. Active engagement in the form of music listening, however, does not seem to influence EF. Higher autistic traits are associated with poorer quality of life and a reduced preference for Contemporary music. Arousal seems not elevated in response to self-selected music and not associated with EF and autistic traits. Results concerning psychometric properties of AQ, music preference, personality and music listening on cognitive performance do not fully replicate previous findings from the Western contexts