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A Multigraph-Based Method for Improving Music Recommendation

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Advances in Artificial Intelligence and Applied Cognitive Computing

Abstract

Music recommendation systems have become an important part of the user-centric online music listening experience. However, current automated systems often are not tuned for exploiting the full diversity of a song catalogue, and consequently, discovering new music requires considerable user effort. Another issue is current implementations generally require significant artist metadata, user listening history, or a combination of the two, to generate relevant recommendations. To address the problems with traditional recommendation systems, we propose to represent artist-to-artist relationships as both simple multigraphs and more complicated multidimensional networks. Using data gathered from the MusicBrainz open music encyclopedia, we demonstrate our artist-based networks are capable of producing more diverse and relevant artist recommendations.

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Correspondence to Qingguo Wang .

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Appendix A: Comparison of Three Node Importance Measurements in Two Multigraphs

Appendix A: Comparison of Three Node Importance Measurements in Two Multigraphs

Rank

Graph I with The Who, The Rolling Stones, Van Halen, The Eagles, and Boston as seed artists

Graph II with R.E.M., Spice Girls, Megadeth, The Cardigans, and Abba as seed artists

Artist

Degree centrality

Load centrality

Page rank

Artist

Degree centrality

Load centrality

Page rank

1

Black

4.181

0.038

0.011

Metallica

2.441

0.007

0.007

2

The Rolling Stones

3.598

0.046

0.010

Black Sabbath

2.568

0.008

0.007

3

Genesis

4.098

0.018

0.010

R.E.M.

2.047

0.056

0.007

4

Whitesnake

3.960

0.016

0.010

The Smiths

2.453

0.010

0.007

5

Yes

4.014

0.009

0.009

Motörhead

2.378

0.008

0.007

6

Deep Purple

3.768

0.014

0.009

Whitesnake

2.408

0.008

0.007

7

Queen

3.691

0.013

0.009

Megadeth

1.676

0.043

0.007

8

Alice Cooper

3.443

0.018

0.008

Queen

2.319

0.012

0.006

9

The Who

3.248

0.027

0.008

The Pretenders

2.371

0.003

0.006

10

Electric Light Orchestra

3.640

0.004

0.008

Ozzy Osbourne

2.286

0.010

0.006

11

Ozzy Osbourne

3.394

0.014

0.008

The Damned

2.029

0.021

0.006

12

Artists united against apartheid

0.973

0.064

0.008

Alice Cooper

2.180

0.009

0.006

13

Uriah Heep

3.520

0.003

0.008

New Order

2.221

0.006

0.006

14

Eric Clapton

3.547

0.003

0.008

Simple Minds

2.209

0.003

0.006

15

The Kinks

3.549

0.003

0.008

Fleetwood Mac

2.185

0.006

0.006

16

Frank Zappa

3.242

0.010

0.008

Killing Joke

2.082

0.007

0.006

17

The Yardbirds

3.229

0.009

0.008

The Who

1.985

0.010

0.006

18

Santana

3.056

0.009

0.007

Ac/Dc

1.965

0.005

0.005

19

Brian May

3.152

0.004

0.007

The Lightning Seeds

1.980

0.005

0.005

Rank

Graph I with The Who, The Rolling Stones, Van Halen, The Eagles, and Boston as seed artists

Graph II with R.E.M., Spice Girls, Megadeth, The Cardigans, and Abba as seed artists

Artist

Degree centrality

Load centrality

Page rank

Artist

Degree centrality

Load centrality

Page rank

20

Faces

2.853

0.016

0.007

Oasis

1.908

0.007

0.005

21

Boston

2.899

0.014

0.007

Tool

1.882

0.005

0.005

22

Def Leppard

3.046

0.004

0.007

The Hollies

1.934

0.004

0.005

23

The Beach Boys

3.166

0.002

0.007

Radiohead

1.839

0.008

0.005

24

Van Halen

2.672

0.023

0.007

Nine Inch Nails

1.786

0.006

0.005

25

Jethro Tull

3.061

0.004

0.007

Modest Mouse

1.456

0.026

0.005

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Waggoner, J., Dunkleman, R., Gao, Y., Gary, T., Wang, Q. (2021). A Multigraph-Based Method for Improving Music Recommendation. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_47

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  • DOI: https://doi.org/10.1007/978-3-030-70296-0_47

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