Music Personalized Label Clustering and Recommendation Visualization

Huo, Yongkang

doi:10.1155/2021/5513355

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Typically, this method employs discrete tags and juxtaposes and levels user tags and music tags, which does not accurately reflect the significance and ranking order relationship of each tag or the cognitive sequence of users as they listen to and annotate music. The user-tag and music-tag data are associated through the tag sequence of tag and music-tag data are correlated and analytically modeled, and feature directed graphs are produced in order to solve this issue and improve recommendation accuracy (Huo, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

Faruk¹,

GHOSH²

2022

EPSTEM

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In the modern scenario, everyone uses the internet to find music, movies, products, services and other commodities on a regular basis to make their lives easier. Because of a lot of data on millions of music, movie, products and services on websites, we need a recommender system very much to assist people in making decisions more quickly and easily. In this research study, we have developed an intelligent music recommendation system by integrating a Music Genre Classification (MGC) with different types of Deep Learning Techniques such as RNN-LSTM, GRU and CNN. We have used the GTZAN Genre dataset to training our system. We have extracted the features from GTZAN dataset by the help of Mel Frequency Cepstral Coefficients (MFCCs) then pass the MFCCs into our deep learning networks. After classifying the appropriate music genre, recommended the music from particular genre from the labelled database which has been classified by our system. From our proposed models the GRU, CNN and RNN-LSTM produced about 71%, 72% and 74% respectively in our testing accuracy. The RNN-LSTM has achieved the best accuracy result (74%) among all of our proposed models.

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Section: Literature Reviewmentioning

confidence: 99%

Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

Faruk¹,

GHOSH²

2022

EPSTEM

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“…Despite these advancements, there are still challenges in the field of music recommendation systems. For instance, the design of personalized music recommendation systems based on directed tags aims to provide basic music services to users and push personalized music recommendation lists, but further research is needed to enhance the effectiveness of such systems [26]. Moreover, the construction of a personalized recommendation system for pop music based on big data analysis highlights the importance of considering internal song characteristics such as melody and beat to calculate music similarity for recommendations [16].…”

Section: Introductionmentioning

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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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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“…To date, many researchers have studied playlists and the personalization of music recommendations [5][6][7][8][9][10][11][12]. Many of these studies have used information about users' preferences for music to recommend music.…”

Section: Introductionmentioning

confidence: 99%

“…However, it did not design a playlist that considered the user's emotions and moods. Huo proposed a method for recommending music suitable for individuals using tags added by each user [6]. It recommended other audio tracks that have tags similar to the tags assigned to the tracks by each user.…”

Section: Introductionmentioning

confidence: 99%

Personalized Music Playlist Generation Method for Placing the Listener in a Positive Mood

MORIZUMI

Ogino

2022

IJAE

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This study proposes a method for the automatic design of personalized playlists that places a listener in a positive mood (uplifting or relaxing feelings) based on individual's impressions of audio tracks. This method designs a playlist with gradually changing personal impressions of audio tracks and induces individuals' moods to be uplifting or relaxing. It estimates the subjective impressions of all audio tracks in the music database using the bagging method, a type of ensemble learning, and creates a personalized playlist. This study investigated the changes in a listener's mood before and after listening to personalized playlists, non-personalized playlists, and playlists with randomly selected tracks using a psychological scale. Consequently, many personalized playlists have shifted the listener into a positive mood more than non-personalized playlists or random design playlists.

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Music Personalized Label Clustering and Recommendation Visualization

Cited by 4 publications

References 22 publications

Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

Developing Music Recommendation System by Integrating an MGC with Deep Learning Techniques

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

Personalized Music Playlist Generation Method for Placing the Listener in a Positive Mood

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