Five-Factor Musical Preference Prediction for Solving New User Cold-Start Problem in Content-Based Music Recommender System

Keisuke Okada, Phan Xuan Tan, Eiji Kamioka

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Recent years witness a boom in music recommender systems due to the success of online streaming services. Even though such systems have brought relatively high-quality recommendations to the users, they are still facing the cold-start problem, especially for new user case. This problem happens when the system does not have information about the new user's preferences to provide recommendations. Therefore, effectively predicting musical preferences for the new user becomes vital. In this paper, we leverage a five-factor MUSIC model which is characterized by Mellow, Unpretentious, Sophisticated, Intense, and Contemporary to represent the user's preference. Then, towards solving the new user cold-start problems in the content-based music recommender system, we propose a method to predict the five-factor preference profile of the novel user. We consider an early-stage scenario when there are no and few rating data of the user available in the system. Accordingly, we first use the information of age and brain type extracted from questionnaires to build regression models. These models are used to predict the first five-factor musical preference profile for initial recommendations. We then estimate the second five-factor profile based on the user's rating data and linearly combine it with the first profile for improving recommendations. The results demonstrated the effectiveness of the proposed method in predicting the musical preference of the new user in the assumed scenario.

本文言語English
ホスト出版物のタイトルIISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665400329
DOI
出版ステータスPublished - 2021 7月 12
イベント12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021 - Virtual, Chania Crete, Greece
継続期間: 2021 7月 122021 7月 14

出版物シリーズ

名前IISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications

Conference

Conference12th International Conference on Information, Intelligence, Systems and Applications, IISA 2021
国/地域Greece
CityVirtual, Chania Crete
Period21/7/1221/7/14

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ サイエンスの応用
  • 情報システム
  • 情報システムおよび情報管理

フィンガープリント

「Five-Factor Musical Preference Prediction for Solving New User Cold-Start Problem in Content-Based Music Recommender System」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル