ContextPlay

Jin, Yucheng; Htun, Nyi Nyi; Tintarev, Nava; Verbert, Katrien

doi:10.1145/3320435.3320445

Cited by 25 publications

(7 citation statements)

References 41 publications

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“…Their capacity to process audio data and generate latent representations, without relying on traditional CF matrix factorization, positions CNNs as a remarkable tool for understanding the latent space of user behavior. This not only enhances speech recognition technologies but also provides valuable insights into user behavior with contextual data from musical content [97][98][99][100][101].…”

Section: Convolutional Neural Network User Modeling Recommendation Al...mentioning

confidence: 98%

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

Amiri,

Shahverdi,

Haddadi

et al. 2024

IEEE Access

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The study of Music Recommender Systems (MRS) has become a crucial field in the age of digital music consumption, influencing how people discover and interact with music. This comprehensive analysis examines the complex field of MRS research from 2005 to 2023, With the growing importance of music recommendation systems in enhancing user experience, it is crucial to comprehend their development. By utilizing rigorous social network analysis, statistical measures, and factor analysis, our investigation not only identifies important themes and influential contributors but also emphasizes the complex and diverse nature of MRS. The trend of the field exhibits a significant increase between 2017 and 2021, with periodic oscillations that highlight its dynamic nature. This analysis offers a broad perspective by examining highly cited articles, current researchers, and local sources. Factorial analysis uncovers thematic clusters, highlighting collaborative filtering, user experience, emotion identification, and reinforcement learning. A scientific mapping analysis classifies research themes in different historical periods, with a focus on important areas such as collaborative filtering, hybrid recommendation, sentiment analysis, and emotion identification. A review of thematic evolution highlights the importance of digitalization, emotion recognition, personalization, user experience, and collaborative filtering in determining the future directions of research. Although there has been a recent decrease in general interest, the investigation of context-aware models and hybrid techniques offers encouraging opportunities for further inquiry. This research enhances our comprehension of MRS dynamics, leading to future improvements and developments in the field. Ultimately, it improves the music discovery experience for people globally.

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Section: Convolutional Neural Network User Modeling Recommendation Al...mentioning

confidence: 98%

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

Amiri,

Shahverdi,

Haddadi

et al. 2024

IEEE Access

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“…For example, after automatically extracting the sound characteristics of a song, and from previously trained models, it is possible to determine the musical genre or the evoked emotions, among others [33,37,44]. EBF techniques are based entirely on the recognition of emotions in music and often involves emotional labeling processes to classify music by emotions and to make recommendations based on the emotion the listener wants to perceive [10,25,37]. PA focuses on suggesting very specific music considering the particular preferences of each user avoiding the different biases that can generate a recommender system based on a general community and the popularity of the artists [37,54].…”

Section: Recommendation Strategiesmentioning

confidence: 99%

“…PA focuses on suggesting very specific music considering the particular preferences of each user avoiding the different biases that can generate a recommender system based on a general community and the popularity of the artists [37,54]. Finally, UC analyzes all the users' contextual variables, such as their current activity, the day of the week, weather, among others, and consequently determines the recommendations [25,28].…”

Section: Recommendation Strategiesmentioning

confidence: 99%

ENSA dataset: a dataset of songs by non-superstar artists tested with an emotional analysis based on time-series

Ospitia-Medina

Beltrán

Baldassarri

2023

Pers Ubiquit Comput

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This paper presents a novel dataset of songs by non-superstar artists in which a set of musical data is collected, identifying for each song its musical structure, and the emotional perception of the artist through a categorical emotional labeling process. The generation of this preliminary dataset is motivated by the existence of biases that have been detected in the analysis of the most used datasets in the field of emotion-based music recommendation. This new dataset contains 234 min of audio and 60 complete and labeled songs. In addition, an emotional analysis is carried out based on the representation of dynamic emotional perception through a time-series approach, in which the similarity values generated by the dynamic time warping (DTW) algorithm are analyzed and then used to implement a clustering process with the K-means algorithm. In the same way, clustering is also implemented with a Uniform Manifold Approximation and Projection (UMAP) technique, which is a manifold learning and dimension reduction algorithm. The algorithm HDBSCAN is applied for determining the optimal number of clusters. The results obtained from the different clustering strategies are compared and, in a preliminary analysis, a significant consistency is found between them. With the findings and experimental results obtained, a discussion is presented highlighting the importance of working with complete songs, preferably with a well-defined musical structure, considering the emotional variation that characterizes a song during the listening experience, in which the intensity of the emotion usually changes between verse, bridge, and chorus.

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“…Item diversity can also be attached to user diversity. Jin, Tintarev, and Verbert (2018) consider the recommendation of musical pieces and seek to enhance the diversity of recommended items (in simple terms, not recommend the same old song). They state that “diversity refers to the diversity of a recommended list measured by an intra-list similarity metric,” which speaks to their understanding of diversity as item diversity (p. 293).…”

Section: Diversity Concepts In Cs and Technology Developmentmentioning

confidence: 99%

Diversity Concepts in Computer Science and Technology Development: A Critique

Schelenz¹

2022

Science, Technology, & Human Values

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Diversity has become increasingly relevant in computer science and technology development, both in terms of inclusive design teams and as a concept used to design “diversity-aware” technologies. The latter part has received less attention and notions of diversity leveraged to design technologies remain understudied. This paper critically examines diversity concepts employed by computer scientists and designers by reviewing 120 articles in two subfields of computer science: personalized recommendations and human–computer interaction. Drawing on Black feminism and critical race theory-inspired literature, I offer a critique of demographic and cultural user representations and underscore the shortcomings of diversity as a strategy to increase user satisfaction and fairness. Particularly concerning is the unreflected use of binary, static, and individual-level diversity concepts in the design of technologies. Such concepts render structural inequalities between and within groups of users invisible and thereby risk reinforcing existing injustices. The paper closes with considerations on whether and how diversity concepts can be leveraged by designers in a social justice–oriented manner.

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ContextPlay

Cited by 25 publications

References 41 publications

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

ENSA dataset: a dataset of songs by non-superstar artists tested with an emotional analysis based on time-series

Diversity Concepts in Computer Science and Technology Development: A Critique

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