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Music is often both personally and affectively meaningful to human listeners. However, little work has been done to create music recommender systems that take this into account. In this demo proposal, we present Spotivibes: a first prototype for a new color-based tagging and music recommender system. This innovative tagging system is designed to take the users' personal experience of music into account and allows them to tag their favorite songs in a non-intrusive way, which can be generalized to their entire library. The goal of Spotivibes is twofold: to help users better tag their playlists to get better playlists and to provide research data on implicit grouping mechanisms in personal music collections. The system was tested with a user study on 34 Spotify users.

Original languageEnglish
Title of host publicationIntRS 2019 Interfaces and Human Decision Making for Recommender Systems 2019
Subtitle of host publicationProceedings of the 6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with 13th ACM Conference on Recommender Systems(RecSys 2019)
EditorsPeter Brusilovsky, Marco de Gemmis, Alexander Felfernig , Pasquale Lops, John O’Donovan, Giovanni Semeraro , Martijn C. Willemsen
PublisherCEUR-WS.org
Pages55-59
Number of pages5
Publication statusPublished - 2019
Event6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2019 - Copenhagen, Denmark
Duration: 19 Sep 201919 Sep 2019

Publication series

NameCEUR Workshop Proceedings
Volume2450
ISSN (Print)1613-0073

Conference

Conference6th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS 2019
CountryDenmark
CityCopenhagen
Period19/09/1919/09/19

    Research areas

  • Color-based tags, Emotion-based recommendations, Personal experience of music, Recommender systems

ID: 68758434