Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research
The study of Music Recommender Systems (MRS) has become crucial in 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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10495033/ |
| _version_ | 1797206135332143104 |
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| author | Babak Amiri Nikan Shahverdi Amirali Haddadi Yalda Ghahremani |
| author_facet | Babak Amiri Nikan Shahverdi Amirali Haddadi Yalda Ghahremani |
| author_sort | Babak Amiri |
| collection | DOAJ |
| description | The study of Music Recommender Systems (MRS) has become crucial in 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 utilising rigorous social network analysis, statistical measures, and factor analysis, our investigation not only identifies essential themes and influential contributors but also emphasises the complex and diverse nature of MRS. The field trend significantly increased between 2017 and 2021, with periodic oscillations highlighting 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, focusing on essential areas such as collaborative filtering, hybrid recommendation, sentiment analysis, and emotion identification. A review of thematic evolution highlights the importance of digitalisation, emotion recognition, personalisation, user experience, and collaborative filtering in determining future research directions. Although there has been a recent decrease in general interest, investigating 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. |
| first_indexed | 2024-04-24T09:02:12Z |
| format | Article |
| id | doaj.art-c227599760d74a08b72f86c656609bb9 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T09:02:12Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c227599760d74a08b72f86c656609bb92024-04-15T23:00:56ZengIEEEIEEE Access2169-35362024-01-0112515005152210.1109/ACCESS.2024.338668410495033Beyond the Trends: Evolution and Future Directions in Music Recommender Systems ResearchBabak Amiri0https://orcid.org/0000-0001-9469-5648Nikan Shahverdi1Amirali Haddadi2Yalda Ghahremani3School of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranSchool of Industrial Engineering, Iran University of Science and Technology, Tehran, IranThe study of Music Recommender Systems (MRS) has become crucial in 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 utilising rigorous social network analysis, statistical measures, and factor analysis, our investigation not only identifies essential themes and influential contributors but also emphasises the complex and diverse nature of MRS. The field trend significantly increased between 2017 and 2021, with periodic oscillations highlighting 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, focusing on essential areas such as collaborative filtering, hybrid recommendation, sentiment analysis, and emotion identification. A review of thematic evolution highlights the importance of digitalisation, emotion recognition, personalisation, user experience, and collaborative filtering in determining future research directions. Although there has been a recent decrease in general interest, investigating 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.https://ieeexplore.ieee.org/document/10495033/Music recommender systemthematic evolutionfuture trendssocial network analysisfactor analysis |
| spellingShingle | Babak Amiri Nikan Shahverdi Amirali Haddadi Yalda Ghahremani Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research IEEE Access Music recommender system thematic evolution future trends social network analysis factor analysis |
| title | Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research |
| title_full | Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research |
| title_fullStr | Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research |
| title_full_unstemmed | Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research |
| title_short | Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research |
| title_sort | beyond the trends evolution and future directions in music recommender systems research |
| topic | Music recommender system thematic evolution future trends social network analysis factor analysis |
| url | https://ieeexplore.ieee.org/document/10495033/ |
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