Personalized Analysis and Recommendation of Aesthetic Evaluation Index of Dance Music Based on Intelligent Algorithm

In the era of Industry 4.0 and 5G, various dance music websites provide thousands of dances and songs, which meet people's needs for dance music and bring great convenience to people. However, the rapid development of dance music has caused the overload of dance music information. Faced with a large number of dances and songs, it is difficult for people to quickly find dance music that conforms to their own interests. The emergence of dance music recommendation system can recommend dance music that users may like and help users quickly discover or find their favorite dances and songs. This kind of recommendation service can provide users with a good experience and bring commercial benefits, so the field of dance music recommendation has become the research direction of industry and scholars. According to different groups of individual aesthetic standards of dance music, this paper introduces the idea of relation learning into dance music recommendation system and applies the relation model to dance music recommendation. In the experiment, the accuracy and recall rate are used to verify the effectiveness of the model in the direction of dance music recommendation.

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Distributing Workflows over a Ubiquitous P2P Network

Scientific Programming ◽

10.1155/2007/191393 ◽

2007 ◽

Vol 15 (4) ◽

pp. 269-281 ◽

Cited By ~ 8

Author(s):

Eddie Al-Shakarchi ◽

Pasquale Cozza ◽

Andrew Harrison ◽

Carlo Mastroianni ◽

Matthew Shields ◽

...

Keyword(s):

Input Data ◽

Recommendation System ◽

Statistical Data ◽

Distributed Database ◽

Peer Networks ◽

P2p Network ◽

Music Recommendation ◽

Peer To Peer Networks ◽

Music Information ◽

Music Recommendation System

This paper discusses issues in the distribution of bundled workflows across ubiquitous peer-to-peer networks for the application of music information retrieval. The underlying motivation for this work is provided by the DART project, which aims to develop a novel music recommendation system by gathering statistical data using collaborative filtering techniques and the analysis of the audio itsel, in order to create a reliable and comprehensive database of the music that people own and which they listen to. To achieve this, the DART scientists creating the algorithms need the ability to distribute the Triana workflows they create, representing the analysis to be performed, across the network on a regular basis (perhaps even daily) in order to update the network as a whole with new workflows to be executed for the analysis. DART uses a similar approach to BOINC but differs in that the workers receive input data in the form of a bundled Triana workflow, which is executed in order to process any MP3 files that they own on their machine. Once analysed, the results are returned to DART's distributed database that collects and aggregates the resulting information. DART employs the use of package repositories to decentralise the distribution of such workflow bundles and this approach is validated in this paper through simulations that show that suitable scalability is maintained through the system as the number of participants increases. The results clearly illustrate the effectiveness of the approach.

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DART

Intelligent Music Information Systems ◽

10.4018/978-1-59904-663-1.ch010 ◽

2011 ◽

pp. 217-234

Author(s):

Eddie Al-Shakarchi ◽

Ian Taylor

Keyword(s):

Signal Processing ◽

Information Retrieval ◽

Recommendation System ◽

Distributed Processing ◽

Research Tool ◽

Music Recommendation ◽

Audio Retrieval ◽

History Of ◽

Music Information ◽

Music Recommendation System

This chapter introduces the DART (Distributed Audio Retrieval using Triana) project as a framework for facilitating the distributed processing and analysis of audio and Music Information Retrieval. The chapter begins by discussing the background and history of Grid and P2P technologies, the Triana framework, the current tools already developed for audio-rate signal processing, and also gives a description of how Triana is employing a decentralized P2P framework to support MIR applications. A music recommendation system is proposed to demonstrate the DART framework, and the chapter also documents the DART team’s progress towards the creation of a working system. The authors hope that introducing the DART system to the MIR community will not only inform them of a research tool that will benefit the entire field of MIR, but also establish DART as an important tool for the rest of the audio and research communities.

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Hybrid Music Recommendation System Using Content-based Filtering and K-Mean Clustering Algorithm

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v6i7.14981501 ◽

2018 ◽

Vol 6 (7) ◽

pp. 1498-1501

Author(s):

Karishma Mandloi ◽

Amit Mittal

Keyword(s):

Clustering Algorithm ◽

Recommendation System ◽

Music Recommendation ◽

Content Based Filtering ◽

Music Recommendation System

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Design and Implementation of Hybrid Music Recommendation System Based on Music Gene

Proceedings of the 2020 International Conference on Computers, Information Processing and Advanced Education ◽

10.1145/3419635.3419669 ◽

2020 ◽

Author(s):

Linfei Wang

Keyword(s):

Recommendation System ◽

Music Recommendation ◽

Design And Implementation ◽

Music Recommendation System

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Neural Collaborative For Music Recommendation System

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1071/1/012021 ◽

2021 ◽

Vol 1071 (1) ◽

pp. 012021

Author(s):

Abba Suganda Girsang ◽

Antoni Wibowo ◽

Jason ◽

Roslynlia

Keyword(s):

Recommendation System ◽

Music Recommendation ◽

Music Recommendation System

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Song Recommendations Based on Artists with Cosine Similarity Algorithms and K-Nearest Neighbor

JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) ◽

10.24843/jlk.2020.v08.i04.p01 ◽

2020 ◽

Vol 8 (4) ◽

pp. 367

Author(s):

Muhammad Arief Budiman ◽

Gst. Ayu Vida Mastrika Giri

Keyword(s):

Collaborative Filtering ◽

Mobile Phones ◽

Recommendation System ◽

Nearest Neighbor ◽

Cosine Similarity ◽

Music Recommendation ◽

K Nearest Neighbor ◽

Filtering Method ◽

K Nearest Neighbor Algorithm ◽

Music Recommendation System

The development of the music industry is currently growing rapidly, millions of music works continue to be issued by various music artists. As for the technologies also follows these developments, examples are mobile phones applications that have music subscription services, namely Spotify, Joox, GrooveShark, and others. Application-based services are increasingly in demand by users for streaming music, free or paid. In this paper, a music recommendation system is proposed, which the system itself can recommend songs based on the similarity of the artist that the user likes or has heard. This research uses Collaborative Filtering method with Cosine Similarity and K-Nearest Neighbor algorithm. From this research, a system that can recommend songs based on artists who are related to one another is generated.

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