Personalized Music Recommendation System using Hybrid Deep Birch Data Analytics Method

Vijayashanthi, V.; Mary, L. Jenitha; Navaneethakrishan, M.; Ramya, M; Mohanaprakash, T. A.; Diviyasri, S.

doi:10.1109/icmnwc56175.2022.10031988

Cited by 1 publication

(2 citation statements)

References 11 publications

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“…Additionally, content-based filtering and unattended learning models offer insightful analysis of specific playlist features. This allows for the provision of recommendations categorized by genre, artist, and even mixed suggestions [51,52]. The overarching objective of these personalized music recommendation systems is to elevate user satisfaction and optimize recommendation effectiveness, thus enriching the overall listening experience.…”

Section: G Personalizationmentioning

confidence: 99%

“…CNNs have showcased remarkable progress in speech recognition and large-scale image classification, surpassing traditional models. Their success can be attributed to key elements like rectified linear units (ReLUs), parallelization for accelerated training through GPU acceleration, and access to substantial training data, such as the Million Song Dataset (MSD) [52,53,54,55].…”

Section: Highly Developed and Isolated Themes: Convolutional Neural N...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: G Personalizationmentioning

confidence: 99%

Section: Highly Developed and Isolated Themes: Convolutional Neural N...mentioning

confidence: 99%

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

Amiri,

Shahverdi,

Haddadi

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Personalized Music Recommendation System using Hybrid Deep Birch Data Analytics Method

Cited by 1 publication

References 11 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

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