Emotion-based Music Recommender System using Viola-Jones and CNN

Dharne, Bijal; Shukla, Naveen; Hooda, Ankit; Jaiswal, Prof. Ichhanshu

doi:10.22214/ijraset.2023.50825

Cited by 1 publication

(4 citation statements)

References 2 publications

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“…One such approach involves leveraging user-centric artificial intelligence, such as enhanced CF. By incorporating user actions beyond explicit ratings, CF addresses sparse data challenges, thereby generating high-quality recommendations [50]. Another effective strategy revolves around disentangling the latent factors embedded within user-item interactions to unveil their underlying dependencies.…”

Section: G Personalizationmentioning

confidence: 99%

“…Another effective strategy revolves around disentangling the latent factors embedded within user-item interactions to unveil their underlying dependencies. Techniques like variational inference and latent structure learning are deployed to replicate observed interaction patterns and meet causal prerequisites [50]. Additionally, content-based filtering and unattended learning models offer insightful analysis of specific playlist features.…”

Section: G Personalizationmentioning

confidence: 99%

“…In tandem, music recommendation systems utilize clustering algorithms like k-means to group tracks based on predicted emotions, thus furnishing song recommendations aligned with users' emotional states. The integration of emotion recognition into music recommender systems not only enhances the accuracy of emotion detection but also elevates the overall user experience, fostering deeper engagement and satisfaction [50,[58][59][60][61] J.…”

Section: Emotion Recognitionmentioning

confidence: 99%

“…This technique leverages the known preferences of a group of users to predict the undiscovered preferences of others. Utilizing a database of user-item interactions, CF forecasts additional topics or products that a new user may find appealing [50].…”

Section: Motor Themes: Collaborative Filteringmentioning

confidence: 99%

See 3 more Smart Citations

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: G Personalizationmentioning

confidence: 99%