research-article

Controlling Spotify Recommendations: Effects of Personal Characteristics on Music Recommender User Interfaces

Authors:

Martijn Millecamp,

Katrien VerbertAuthors Info & Claims

UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

Pages 101 - 109

https://doi.org/10.1145/3209219.3209223

Published: 03 July 2018 Publication History

Abstract

The "black box'' nature of today's recommender systems raises a number of challenges for users, including a lack of trust and limited user control. Providing more user control is interesting to enable end-users to help steer the recommendation process with additional input and feedback. However, different users may have different preferences with regard to such control. To the best of our knowledge, no research has investigated the effect of personal characteristics on visual control techniques in the music recommendation domain. In this paper, we present results of a user study on the web using two different visualisation techniques (a radar chart and sliders) that allows users to control Spotify recommendations. A within-subject design withLatin Square counterbalancing measures was used for the study. Results indicate that the radar chart helped the participants discover a significantly higher number of new songs compared to the sliders. We also found that users' experience with Spotify had an influence on their interaction with different musical attributes. The participants who used Spotify frequently and users with a high individual musical sophistication interacted with the attributes significantly more with the radar chart compared to the sliders. Individual musical sophistication also had a significant impact on their interaction with the interaction techniques. The participants with high musical sophistication interacted significantly more with the radar chart in comparison to the sliders. Based on the feedback from our participants, we provide design suggestions to further improve user control in music recommendation.

References

[1]

Azzah Al-Maskari and Mark Sanderson. 2011. The effect of user characteristics on search effectiveness in information retrieval. Information Processing & Management Vol. 47, 5 (2011), 719--729.

Digital Library

[2]

Ivana Andjelkovic, Denis Parra, and John O'Donovan. 2016. Moodplay: Interactive Mood-based Music Discovery and Recommendation Proceedings of the 2016 Conference on User Modeling Adaptation and Personalization. ACM, 275--279.

Digital Library

[3]

Nuray M Aykin and Turgut Aykin. 1991. Individual differences in human-computer interaction. Computers & industrial engineering Vol. 20, 3 (1991), 373--379.

Digital Library

[4]

Svetlin Bostandjiev, John O'Donovan, and Tobias Höllerer. 2012. TasteWeights: a visual interactive hybrid recommender system Proc. RecSys'12. ACM, 35--42.

Digital Library

[5]

Svetlin Bostandjiev, John O'Donovan, and Tobias Höllerer. 2013. LinkedVis: exploring social and semantic career recommendations Proceedings of the 2013 international conference on Intelligent user interfaces. ACM, 107--116.

Digital Library

[6]

Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology Vol. 3, 2 (2006), 77--101.

[7]

Simon Bruns, André Calero Valdez, Christoph Greven, Martina Ziefle, and Ulrik Schroeder. 2015. What should i read next? a personalized visual publication recommender system International Conference on Human Interface and the Management of Information. Springer, 89--100.

[8]

Peter Brusilovsky and Eva Millán. 2007. User models for adaptive hypermedia and adaptive educational systems. The adaptive web. Springer, 3--53.

Digital Library

[9]

Giuseppe Carenini, Cristina Conati, Enamul Hoque, Ben Steichen, Dereck Toker, and James Enns. 2014. Highlighting interventions and user differences: informing adaptive information visualization support. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 1835--1844.

Digital Library

[10]

Zohreh Dehghani Champiri, Seyed Reza Shahamiri, and Siti Salwah Binti Salim. 2015. A systematic review of scholar context-aware recommender systems. Expert Systems with Applications Vol. 42, 3 (2015), 1743--1758.

Digital Library

[11]

He Chen, Denis Parra, and Katrien Verbert. 2016. Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities. Expert Systems with Applications Vol. 56 (2016), 9--27.

Digital Library

[12]

Yu Chen and Pearl Pu. 2013. Cofeel: Using emotions to enhance social interaction in group recommender systems Alpine Rendez-Vous (ARV) 2013 Workshop on Tools and Technology for Emotion-Awareness in Computer Mediated Collaboration and Learning.

[13]

Cristina Conati, Giuseppe Carenini, Enamul Hoque, Ben Steichen, and Dereck Toker. 2014. Evaluating the impact of user characteristics and different layouts on an interactive visualization for decision making. In Computer Graphics Forum, Vol. Vol. 33. Wiley Online Library, 371--380.

[14]

Gitta O Domik and Bernd Gutkauf. 1994. User modeling for adaptive visualization systems Visualization, 1994., Visualization'94, Proceedings., IEEE Conference on. IEEE, 217--223.

Digital Library

[15]

Michael D Ekstrand, Daniel Kluver, F Maxwell Harper, and Joseph A Konstan. 2015. Letting Users Choose Recommender Algorithms: An Experimental Study Proceedings of the 9th ACM Conference on Recommender Systems (RecSys '15). ACM, New York, NY, USA, 11--18.

Digital Library

[16]

Susan Gauch, Mirco Speretta, Aravind Chandramouli, and Alessandro Micarelli. 2007. User profiles for personalized information access. The adaptive web. Springer, 54--89.

Digital Library

[17]

Vladimir Geroimenko and Chaomei Chen. 2006. Visualizing the semantic web: XML-based internet and information visualization. Springer, London.

Digital Library

[18]

David M Greenberg, Michal Kosinski, David J Stillwell, Brian L Monteiro, Daniel J Levitin, and Peter J Rentfrow. 2016. The Song Is You: Preferences for Musical Attribute Dimensions Reflect Personality. Social Psychological and Personality Science, Vol. 7, 6 (2016), 597--605.

[19]

Brynjar Gretarsson, John O'Donovan, Svetlin Bostandjiev, Christopher Hall, and Tobias Höllerer. 2010. Smallworlds: visualizing social recommendations. Computer Graphics Forum, Vol. Vol. 29. Wiley Online Library, 833--842.

Digital Library

[20]

Ruth Herbert. 2013. Everyday music listening: Absorption, dissociation and trancing. Ashgate Publishing, Ltd.

[21]

Jonathan L Herlocker, Joseph A Konstan, and John Riedl. 2000. Explaining collaborative filtering recommendations Proceedings of the 2000 ACM conference on Computer supported cooperative work. ACM, 241--250.

Digital Library

[22]

Yoshinori Hijikata, Yuki Kai, and Shogo Nishida. 2012. The Relation Between User Intervention and User Satisfaction for Information Recommendation Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC '12). ACM, New York, NY, USA, 2002--2007.

Digital Library

[23]

Satoru Inoue, Hisae Aoyama, and Keiichi Nakata. 2011. Cognitive analysis for knowledge modeling in air traffic control work International Conference on Human-Computer Interaction. Springer, 341--350.

Digital Library

[24]

Yucheng Jin, Bruno Cardoso, and Katrien Verbert. 2017. How do different levels of user control affect cognitive load and acceptance of recommendations?. In Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2017). CEUR-WS, 35--42.

[25]

Antti Kangasr"a"asiö, Dorota Glowacka, and Samuel Kaski. 2015. Improving controllability and predictability of interactive recommendation interfaces for exploratory search. In Proceedings of the 20th international conference on intelligent user interfaces. ACM, 247--251.

Digital Library

[26]

Roy PC Kessels, Martine JE Van Zandvoort, Albert Postma, L Jaap Kappelle, and Edward HF De Haan . 2000. The Corsi block-tapping task: standardization and normative data. Applied neuropsychology Vol. 7, 4 (2000), 252--258.

[27]

Bart P Knijnenburg, Niels J M Reijmer, and Martijn C Willemsen . 2011. Each to His Own: How Different Users Call for Different Interaction Methods in Recommender Systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys '11). ACM, New York, NY, USA, 141--148.

Digital Library

[28]

Zacharias Lekkas, Nikos Tsianos, Panagiotis Germanakos, Constantinos Mourlas, and George Samaras . 2011. The effects of personality type in user-centered appraisal systems International Conference on Human-Computer Interaction. Springer, 388--396.

Digital Library

[29]

Benedikt Loepp, Katja Herrmanny, and Jürgen Ziegler . 2015. Blended recommending: Integrating interactive information filtering and algorithmic recommender techniques. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. ACM, 975--984.

Digital Library

[30]

John O'Donovan, Barry Smyth, Brynjar Gretarsson, Svetlin Bostandjiev, and Tobias Höllerer . 2008. PeerChooser: visual interactive recommendation. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1085--1088.

Digital Library

[31]

Denis Parra and Peter Brusilovsky . 2015. User-controllable personalization: A case study with SetFusion. International Journal of Human-Computer Studies Vol. 78 (2015), 43--67.

Digital Library

[32]

Pearl Pu, Li Chen, and Rong Hu . 2011. A user-centric evaluation framework for recommender systems Proc. RecSys'11. ACM, 157--164.

Digital Library

[33]

James Schaffer, Tobias Höllerer, and John O'Donovan . 2015. Hypothetical Recommendation: A Study of Interactive Profile Manipulation Behavior for Recommender Systems. In FLAIRS Conference. 507--512.

[34]

Rashmi Sinha and Kirsten Swearingen . 2002. The role of transparency in recommender systems. CHI'02 extended abstracts on Human factors in computing systems. ACM, 830--831.

Digital Library

[35]

Dereck Toker, Cristina Conati, Giuseppe Carenini, and Mona Haraty . 2012. Towards adaptive information visualization: on the influence of user characteristics International Conference on User Modeling, Adaptation, and Personalization. Springer, 274--285.

Digital Library

[36]

John Venn . 1880. I. On the diagrammatic and mechanical representation of propositions and reasonings. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, Vol. 10, 59 (. 1880), 1--18.

[37]

Katrien Verbert, Denis Parra, and Peter Brusilovsky . 2016. Agents Vs. Users: Visual Recommendation of Research Talks with Multiple Dimension of Relevance. ACM Transactions on Interactive Intelligent Systems (TiiS), Vol. 6, 2 (2016), 11.

Digital Library

[38]

Katrien Verbert, Denis Parra, Peter Brusilovsky, and Erik Duval . 2013. Visualizing recommendations to support exploration, transparency and controllability Proceedings of the 2013 international conference on Intelligent user interfaces. ACM, 351--362.

Digital Library

[39]

Jesse Vig, Shilad Sen, and John Riedl . 2009. Tagsplanations: explaining recommendations using tags Proceedings of the 14th international conference on Intelligent user interfaces. ACM, 47--56.

Digital Library

[40]

Michail Vlachos and Daniel Svonava . 2012. Graph embeddings for movie visualization and recommendation First International Workshop on Recommendation Technologies for Lifestyle Change (LIFESTYLE 2012). 56.

[41]

David Wong, Siamak Faridani, Ephrat Bitton, Björn Hartmann, and Ken Goldberg . 2011. The diversity donut: enabling participant control over the diversity of recommended responses CHI'11 Extended Abstracts on Human Factors in Computing Systems. ACM, 1471--1476.

Digital Library

[42]

Xiangmin Zhang and Mark Chignell . 2001. Assessment of the effects of user characteristics on mental models of information retrieval systems. Journal of the Association for Information Science and Technology, Vol. 52, 6 (2001), 445--459.

Digital Library

Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
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Szymanski MOoge JDe Croon RVanden Abeele VVerbert K(2024)Feedback, Control, or Explanations? Supporting Teachers With Steerable Distractor-Generating AIProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636933(690-700)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636933
Sun JYang JZhou GJin YGong J(2024)Understanding Human-AI Collaboration in Music Therapy Through Co-Design with TherapistsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642764(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642764
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Index Terms

Controlling Spotify Recommendations: Effects of Personal Characteristics on Music Recommender User Interfaces
1. Human-centered computing
  1. Visualization
    1. Visualization design and evaluation methods
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image ACM Conferences

UMAP '18: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization

July 2018

393 pages

ISBN:9781450355896

DOI:10.1145/3209219

General Chairs:
Tanja Mitrovic
University of Canterbury, New Zealand
,
Jie Zhang
Nanyang Technological University, Singapore
,
Program Chairs:
Li Chen
Hong Kong Baptist University, China
,
David Chin
University of Hawaii, USA

Copyright © 2018 ACM.

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Published: 03 July 2018

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KU Leuven Research Council

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UMAP '18

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UMAP '18: 26th Conference on User Modeling, Adaptation and Personalization

July 8 - 11, 2018

Singapore, Singapore

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UMAP '18 Paper Acceptance Rate 26 of 93 submissions, 28%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Ge YLiu SFu ZTan JLi ZXu SLi YXian YZhang Y(2024)A Survey on Trustworthy Recommender SystemsACM Transactions on Recommender Systems10.1145/3652891Online publication date: 13-Apr-2024
https://doi.org/10.1145/3652891
Szymanski MOoge JDe Croon RVanden Abeele VVerbert K(2024)Feedback, Control, or Explanations? Supporting Teachers With Steerable Distractor-Generating AIProceedings of the 14th Learning Analytics and Knowledge Conference10.1145/3636555.3636933(690-700)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3636555.3636933
Sun JYang JZhou GJin YGong J(2024)Understanding Human-AI Collaboration in Music Therapy Through Co-Design with TherapistsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642764(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642764
Ma YZiegler J(2024)Investigating meta-intents: user interaction preferences in conversational recommender systemsUser Modeling and User-Adapted Interaction10.1007/s11257-024-09411-3Online publication date: 24-Sep-2024
https://doi.org/10.1007/s11257-024-09411-3
Gaspar AGil MPanach JRomero V(2024)Towards a general user model to develop intelligent user interfacesMultimedia Tools and Applications10.1007/s11042-024-18240-w83:26(67501-67534)Online publication date: 25-Jan-2024
https://doi.org/10.1007/s11042-024-18240-w
Murindanyi SNakate AKyebambe MNakibuule RMarvin G(2024)Responsible Artificial Intelligence for Music RecommendationData Science and Applications10.1007/978-981-99-7862-5_22(291-306)Online publication date: 16-Feb-2024
https://doi.org/10.1007/978-981-99-7862-5_22
Afridi AOlsson T(2023)Review of User Interface-Facilitated Serendipity in Recommender SystemsInternational Journal of Interactive Communication Systems and Technologies10.4018/IJICST.32018012:1(1-19)Online publication date: 17-Mar-2023
https://dl.acm.org/doi/10.4018/IJICST.320180
Lv SPan L(2023)BTSAMAInternational Journal of Ambient Computing and Intelligence10.4018/IJACI.32735114:1(1-23)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.4018/IJACI.327351
Ooge JDereu LVerbert K(2023)Steering Recommendations and Visualising Its Impact: Effects on Adolescents’ Trust in E-Learning PlatformsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584046(156-170)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584046
Ma YDonkers TKleemann TZiegler J(2023)An Instrument for measuring users’ meta-intentsProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578317(290-302)Online publication date: 19-Mar-2023
https://dl.acm.org/doi/10.1145/3576840.3578317
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