Evaluating user experience in music discovery on deezer and spotify

Assunção, Willian Garcias de; Zaina, Luciana A. M.

doi:10.1145/3554364.3560901

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Traditional music recommender systems have historically prioritized prediction accuracy over considerations of user experience (UX) when generating recommendations. However, recent research has shed light on the pivotal role of UX in the effectiveness of recommendation systems [62,63]. Factors such as injecting novelty into recommendations, ensuring regular updates, and catering to users' interest in rating contribute significantly to fostering a positive UX [64].…”

Section: User Experiencementioning

confidence: 99%

“…This innovative approach focuses on deciphering the emotional content of music lyrics, providing an alternative to traditional CF methods. The integration of Sentiment Analysis introduces lightweight, language-independent models, contributing to the diversified landscape of MRS research [62,63].…”

Section: Highly Developed and Isolated Themes: Sentiment Analysis Hyb...mentioning

confidence: 99%

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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: User Experiencementioning

confidence: 99%

Section: Highly Developed and Isolated Themes: Sentiment Analysis Hyb...mentioning

confidence: 99%

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

Amiri,

Shahverdi,

Haddadi

et al. 2024

IEEE Access

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MMusic: a hierarchical multi-information fusion method for deep music recommendation

Gan

Zhang

2023

J Intell Inf Syst

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Social music discovery: an ethical recommendation system based on friend’s preferred songs

Furini,

Fragnelli

2024

Multimed Tools Appl

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Music recommendation systems have become ubiquitous in today’s world, but they raise ethical concerns related to bias, discrimination, and lack of transparency. To address these issues, we propose a recommendation system that combines content-based and collaborative filtering approaches within three different recommendation algorithms. These algorithms create playlists that mimic the user’s listening habits while identifying similar tracks within the listening histories of the user’s friends. To evaluate the effectiveness of our system, we asked ten participants to rate a total of ninety playlists. The results showed high satisfaction among participants with the playlists generated by two of the proposed recommendation algorithms. Specifically, participants who preferred to stay within their musical comfort zone appreciated one specific recommendation algorithm, while those who were willing to explore new music tended appreciated the other recommendation algorithm. In summary, by leveraging the user’s social connections, our proposed system provides a more transparent and ethical approach to music recommendations. It provides a personalized and enjoyable music discovery experience that considers the nuances of individual musical taste and preferences. These findings suggest the potential impact of our proposal in addressing ethical concerns and enhancing user satisfaction in music recommendation services.

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Evaluating user experience in music discovery on deezer and spotify

Cited by 4 publications

References 35 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

MMusic: a hierarchical multi-information fusion method for deep music recommendation

Social music discovery: an ethical recommendation system based on friend’s preferred songs

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