The MuTube Dataset for Music Listening History Retrieval and Recommendation System

Yi Chen Wang, Pei Lin Yang, Sok-Ian Sou, Hsun Ping Hsieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recommendation systems are widely used in music streaming services. This paper presents a system to collect user's music preference and music textual features in YouTube as well as to provide music recommendations based on collaborative filtering. As cold start and data sparsity are two severe issues in collaborative filtering, additional features for the item are necessary. We propose a method to aggregate both implicit feedback and textual features collected from YouTube to improve the recommendation performance. Experiment results indicate that the recommendation playlists generated by this system both match individual's and group preference.

Original languageEnglish
Title of host publicationProceedings - 2020 International Computer Symposium, ICS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages55-60
Number of pages6
ISBN (Electronic)9781728192550
DOIs
Publication statusPublished - 2020 Dec
Event2020 International Computer Symposium, ICS 2020 - Tainan, Taiwan
Duration: 2020 Dec 172020 Dec 19

Publication series

NameProceedings - 2020 International Computer Symposium, ICS 2020

Conference

Conference2020 International Computer Symposium, ICS 2020
Country/TerritoryTaiwan
CityTainan
Period20-12-1720-12-19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Information Systems and Management
  • Computational Mathematics

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