2,561 research outputs found
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 italian music superdiversity: Geography, emotion and language: one resource to find them, one resource to rule them all
Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices
Cultural transmission modes of music sampling traditions remain stable despite delocalization in the digital age
Music sampling is a common practice among hip-hop and electronic producers
that has played a critical role in the development of particular subgenres.
Artists preferentially sample drum breaks, and previous studies have suggested
that these may be culturally transmitted. With the advent of digital sampling
technologies and social media the modes of cultural transmission may have
shifted, and music communities may have become decoupled from geography. The
aim of the current study was to determine whether drum breaks are culturally
transmitted through musical collaboration networks, and to identify the factors
driving the evolution of these networks. Using network-based diffusion analysis
we found strong evidence for the cultural transmission of drum breaks via
collaboration between artists, and identified several demographic variables
that bias transmission. Additionally, using network evolution methods we found
evidence that the structure of the collaboration network is no longer biased by
geographic proximity after the year 2000, and that gender disparity has relaxed
over the same period. Despite the delocalization of communities by the
internet, collaboration remains a key transmission mode of music sampling
traditions. The results of this study provide valuable insight into how
demographic biases shape cultural transmission in complex networks, and how the
evolution of these networks has shifted in the digital age
UNDERSTANDING MUSIC TRACK POPULARITY IN A SOCIAL NETWORK
Thousands of music tracks are uploaded to the Internet every day through websites and social networks that focus on music. While some content has been popular for decades, some tracks that have just been released have been ignored. What makes a music track popular? Can the duration of a music track’s popularity be explained and predicted? By analysing data on the performance of a music track on the ranking charts, coupled with the creation of machine-generated music semantics constructs and a variety of other track, artist and market descriptors, this research tests a model to assess how track popularity and duration on the charts are determined. The dataset has 78,000+ track ranking observations from a streaming music service. The importance of music semantics constructs (genre, mood, instrumental, theme) for a track, and other non-musical factors, such as artist reputation and social information, are assessed. These may influence the staying power of music tracks in online social networks. The results show it is possible to explain chart popularity duration and the weekly ranking of music tracks. This research emphasizes the power of data analytics for knowledge discovery and explanation that can be achieved with a combination of machine-based and econometrics-based approaches
Music feature extraction and analysis through Python
En l'era digital, plataformes com Spotify s'han convertit en els principals canals de consum de música, ampliant les possibilitats per analitzar i entendre la música a través de les dades. Aquest projecte es centra en un examen exhaustiu d'un conjunt de dades obtingut de Spotify, utilitzant Python com a eina per a l'extracció i anà lisi de dades. L'objectiu principal es centra en la creació d'aquest conjunt de dades, emfatitzant una à mplia varietat de cançons de diversos subgèneres. La intenció és representar tant el panorama musical més tendenciós i popular com els nÃnxols, alineant-se amb el concepte de distribució de Cua Llarga, terme popularitzat com a "Long Tail" en anglès, que destaca el potencial de mercat de productes de nÃnxols amb menor popularitat. A través de l'anà lisi, es posen de manifest patrons en l'evolució de les caracterÃstiques musicals al llarg de les dècades passades. Canvis en caracterÃstiques com l'energia, el volum, la capacitat de ball, el positivisme que desprèn una cançó i la seva correlació amb la popularitat sorgeixen del conjunt de dades. Paral·lelament a aquesta anà lisi, es concep un sistema de recomanació musical basat en el contingut del conjunt de dades creat. L'objectiu és connectar cançons, especialment les menys conegudes, amb possibles oients. Aquest projecte ofereix perspectives beneficioses per a entusiastes de la música, cientÃfics de dades i professionals de la indústria. Les metodologies implementades i l'anà lisi realitzat presenten un punt de convergència de la ciència de dades i la indústria de la música en el context digital actualEn la era digital, plataformas como Spotify se han convertido en los principales canales de consumo de música, ampliando las posibilidades para analizar y entender la música a través de los datos. Este proyecto se centra en un examen exhaustivo de un conjunto de datos obtenido de Spotify, utilizando Python como herramienta para la extracción y análisis de datos. El objetivo principal se centra en la creación de este conjunto de datos, enfatizando una amplia variedad de canciones de diversos subgéneros. La intención es representar tanto el panorama musical más tendencioso y popular como los nichos, alineándose con el concepto de distribución de Cola Larga, término popularizado como Long Tail en inglés, que destaca el potencial de mercado de productos de nichos con menor popularidad. A través del análisis, se evidencian patrones en la evolución de las caracterÃsticas musicales a lo largo de las décadas pasadas. Cambios en caracterÃsticas como la energÃa, el volumen, la capacidad de baile, el positivismo que desprende una canción y su correlación con la popularidad surgen del conjunto de datos. Paralelamente a este análisis, se concibe un sistema de recomendación musical basado en el contenido del conjunto de datos creado. El objetivo es conectar canciones, especialmente las menos conocidas, con posibles oyentes. Este proyecto ofrece perspectivas beneficiosas para entusiastas de la música, cientÃficos de datos y profesionales de la industria. Las metodologÃas implementadas y el análisis realizado presentan un punto de convergencia de la ciencia de datos y la industria de la música en el contexto digital actualIn the digital era, platforms like Spotify have become the primary channels of music consumption, broadening the possibilities for analyzing and understanding music through data. This project focuses on a comprehensive examination of a dataset sourced from Spotify, with Python as the tool for data extraction and analysis. The primary objective centers around the creation of this dataset, emphasizing a diverse range of songs from various subgenres. The intention is to represent both mainstream and niche musical landscapes, aligning with the Long Tail distribution concept, which highlights the market potential of less popular niche products. Through analysis, patterns in the evolution of musical features over past decades become evident. Shifts in features such as energy, loudness, danceability, and valence and their correlation with popularity emerge from the dataset. Parallel to this analysis is the conceptualization of a music recommendation system based on the content of the data set. The aim is to connect tracks, especially lesser-known ones, with potential listeners. This project provides insights beneficial for music enthusiasts, data scientists, and industry professionals. The methodologies and analyses present a convergence of data science and the music industry in today's digital contex
Modelling Sequential Music Track Skips using a Multi-RNN Approach
Modelling sequential music skips provides streaming companies the ability to
better understand the needs of the user base, resulting in a better user
experience by reducing the need to manually skip certain music tracks. This
paper describes the solution of the University of Copenhagen DIKU-IR team in
the 'Spotify Sequential Skip Prediction Challenge', where the task was to
predict the skip behaviour of the second half in a music listening session
conditioned on the first half. We model this task using a Multi-RNN approach
consisting of two distinct stacked recurrent neural networks, where one network
focuses on encoding the first half of the session and the other network focuses
on utilizing the encoding to make sequential skip predictions. The encoder
network is initialized by a learned session-wide music encoding, and both of
them utilize a learned track embedding. Our final model consists of a majority
voted ensemble of individually trained models, and ranked 2nd out of 45
participating teams in the competition with a mean average accuracy of 0.641
and an accuracy on the first skip prediction of 0.807. Our code is released at
https://github.com/Varyn/WSDM-challenge-2019-spotify.Comment: 4 page
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