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TagFlip: Active Mobile Music Discovery with Social Tags

Authors:

Mohsen Kamalzadeh,

Christoph Kralj,

Torsten Möller,

Michael SedlmairAuthors Info & Claims

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

Pages 19 - 30

https://doi.org/10.1145/2856767.2856780

Published: 07 March 2016 Publication History

Abstract

We report on the design and evaluation of TagFlip, a novel interface for active music discovery based on social tags of music. The tool, which was built for phone-sized screens, couples high user control on the recommended music with minimal interaction effort. Contrary to conventional recommenders, which only allow the specification of seed attributes and the subsequent like/dislike of songs, we put the users in the centre of the recommendation process. With a library of 100,000 songs, TagFlip describes each played song to the user through its most popular tags on Last.fm and allows the user to easily specify which of the tags should be considered for the next song, or the next stream of songs. In a lab user study where we compared it to Spotify's mobile application, TagFlip came out on top in both subjective user experience (control, transparency, and trust) and our objective measure of number of interactions per liked song. Our users found TagFlip to be an important complementary experience to that of Spotify, enabling more active and directed discovery sessions as opposed to the mostly passive experience that traditional recommenders offer.

Supplementary Material

ZIP File (iuifp242.zip)

The supplementary material contains: - Images of prototypes from TagFlip's design process - A brief explanation of how we processed our tag data from Last.fm for use in TagFlip - Histograms for responses to all 22 questions on recommendation aspects - A video showing the operation of TagFlip, recorded from a phone

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Cited By

Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Liang YWillemsen M(2022)Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferencesProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546772(3-13)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546772
Petridis SDaskalova NMennicken SWay SLamere PThom J(2022)TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender SystemsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511156(120-133)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511156
Show More Cited By

Index Terms

TagFlip: Active Mobile Music Discovery with Social Tags
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
    2. Interaction paradigms
      1. Graphical user interfaces
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design

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Published In

cover image ACM Conferences

IUI '16: Proceedings of the 21st International Conference on Intelligent User Interfaces

March 2016

446 pages

ISBN:9781450341370

DOI:10.1145/2856767

General Chairs:
Jeffrey Nichols
Google Inc, USA
,
Jalal Mahmud
IBM Research, USA
,
John O'Donovan
UC Santa Barbara, USA
,
Program Chairs:
Cristina Conati
University of British Columbia, Canada
,
Massimo Zancanaro
Bruno Kessler Foundation, Italy

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 07 March 2016

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March 7 - 10, 2016

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IUI '16 Paper Acceptance Rate 49 of 194 submissions, 25%;

Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

Gabbolini GBridge D(2024)Surveying More Than Two Decades of Music Information Retrieval Research on PlaylistsACM Transactions on Intelligent Systems and Technology10.1145/368839815:6(1-68)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3688398
Liang YWillemsen M(2022)Exploring the longitudinal effects of nudging on users’ music genre exploration behavior and listening preferencesProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546772(3-13)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546772
Petridis SDaskalova NMennicken SWay SLamere PThom J(2022)TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender SystemsProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511156(120-133)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511156
Liang YWillemsen M(2022)Promoting Music Exploration through Personalized Nudging in a Genre Exploration RecommenderInternational Journal of Human–Computer Interaction10.1080/10447318.2022.210806039:7(1495-1518)Online publication date: 21-Aug-2022
https://doi.org/10.1080/10447318.2022.2108060
Assuncao WPiccolo LZaina L(2022)Considering emotions and contextual factors in music recommendation: a systematic literature reviewMultimedia Tools and Applications10.1007/s11042-022-12110-z81:6(8367-8407)Online publication date: 2-Feb-2022
https://dl.acm.org/doi/10.1007/s11042-022-12110-z
Liang YWillemsen M(2021)The role of preference consistency, defaults and musical expertise in users’ exploration behavior in a genre exploration recommenderProceedings of the 15th ACM Conference on Recommender Systems10.1145/3460231.3474253(230-240)Online publication date: 13-Sep-2021
https://dl.acm.org/doi/10.1145/3460231.3474253
Liang YWillemsen MHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Interactive Music Genre Exploration with Visualization and Mood ControlProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450700(175-185)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450700
Cai WJin YChen LHammond TVerbert KParra DKnijnenburg BO'Donovan JTeale P(2021)Critiquing for Music Exploration in Conversational Recommender SystemsProceedings of the 26th International Conference on Intelligent User Interfaces10.1145/3397481.3450657(480-490)Online publication date: 14-Apr-2021
https://dl.acm.org/doi/10.1145/3397481.3450657
Millecamp MHaveneers RVerbert K(2020)Cogito ergo quid? The Effect of Cognitive Style in a Transparent Mobile Music Recommender SystemProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394871(323-327)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3340631.3394871
Kamehkhosh IBonnin GJannach D(2019)Effects of recommendations on the playlist creation behavior of usersUser Modeling and User-Adapted Interaction10.1007/s11257-019-09237-4Online publication date: 22-May-2019
https://doi.org/10.1007/s11257-019-09237-4
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