Examining the Relationship Between Songs and Psychological Characteristics

Pyun, Miran; Kim, Dong-Hun; Lim, Cheol-Il; Lee, Eunbyul; Kwon, Jihey; Lee, Sang Yup

doi:10.1007/978-3-030-60128-7_8

Cited by 1 publication

(1 citation statement)

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“…It should be noted that we did * https://musicbrainz.org/ * * https://developer.spotify.com/documentation/web-api/ not use "mode" because it is a binary value. Instead, we used the float value "instrumentalness", as utilized in [41], to take into account how much singing voice is included in the song. By using these acoustic features, we can achieve music recommendation that takes into account the user's preferences for various aspects of songs.…”

Section: Settingsmentioning

confidence: 99%

[Paper] Deep Reinforcement Learning-based Music Recommendation with Knowledge Graph Using Acoustic Features

Sakurai

Togo

Ogawa

et al. 2022

MTA

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In this study, we propose a new deep reinforcement learning-based music recommendation method with knowledge graphs. With the rapid development of Web services, music-related content posted on platforms, such as YouTube, is increasing dramatically. Conventional recommendation methods based on knowledge graphs have struggled with the coldstart problem caused by a lack of user preference information. The proposed method can solve this problem by introducing acoustic feature edges in the constructed knowledge graph. Furthermore, we realize efficient search using a deep reinforcement learning algorithm on a dense knowledge graph introducing acoustic feature-based edges. The proposed method can make appropriate recommendations even with a small amount of user preference information by learning the optimal action of the agent. We confirm the effectiveness of the proposed method by comparing our method with several conventional and state-of-the-art recommendation methods.

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Section: Settingsmentioning

confidence: 99%