research-article

ContextPlay: Evaluating User Control for Context-Aware Music Recommendation

Authors:

Katrien VerbertAuthors Info & Claims

UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

Pages 294 - 302

https://doi.org/10.1145/3320435.3320445

Published: 07 June 2019 Publication History

Abstract

Music preferences are likely to depend on contextual characteristics such as location and activity. However, most recommender systems do not allow users to adapt recommendations to their current context. We therefore built ContextPlay, a context-aware music recommender that enables user control for both contextual characteristics and music preferences. By conducting a mixed-design study (N=114) with four typical scenarios of music listening, we investigate the effect of controlling contextual characteristics in a music recommender system on four aspects: perceived quality, diversity, effectiveness, and cognitive load. Compared to our baseline which only allows to specify music preferences, having additional control for context leads to higher perceived quality and does not increase cognitive load. We also find that the contexts of mood, weather, and location tend to influence user perception of the system. Moreover, we found that users are more likely to modify contexts and their profile during relaxing activities.

References

[1]

Gediminas Adomavicius and Francesco Ricci. 2009. RecSys' 09 workshop 3: workshop on context-aware recommender systems (CARS-2009). In Proceedings of the third ACM conference on Recommender systems. ACM, 423--424.

Digital Library

[2]

Gediminas Adomavicius and Alexander Tuzhilin. 2005. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge & Data Engineering 6 (2005), 734--749.

Digital Library

[3]

Gediminas Adomavicius and Alexander Tuzhilin. 2011. Context-aware recommender systems. In Recommender systems handbook . Springer, 217--253.

[4]

Judy I Alpert and Mark I Alpert. 1990. Music influences on mood and purchase intentions. Psychology & Marketing, Vol. 7, 2 (1990), 109--133.

[5]

Ivana Andjelkovic, Denis Parra, and John O'Donovan. 2016. Moodplay: Interactive Mood-based Music Discovery and Recommendation. In Proc. of UMAP '16. ACM, 275--279.

Digital Library

[6]

Fedor Bakalov, Marie-Jean Meurs, Birgitta König-Ries, Bahar Sateli, René Witte, Greg Butler, and Adrian Tsang. 2013. An approach to controlling user models and personalization effects in recommender systems. In Proc. of IUI'13. ACM, 49--56.

Digital Library

[7]

Linas Baltrunas and Xavier Amatriain. 2009. Towards time-dependant recommendation based on implicit feedback. In Workshop on context-aware recommender systems (CARS`09). Citeseer.

[8]

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

Digital Library

[9]

Yu Chen and Pearl Pu. 2012. CoFeel: Using Emotions for Social Interaction in Group Recommender Systems. In First International Workshop on Recommendation Technologies for Lifestyle Change (LIFESTYLE 2012). 48.

[10]

Pieter Walter de Vries. 2004. Trust in systems: effects of direct and indirect information .Technische Universiteit Eindhoven.

[11]

Chaima Dhahri, Kazunori Matsumoto, and Keiichiro Hoashi. 2018. Mood-Aware Music Recommendation via Adaptive Song Embedding. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) . IEEE, 135--138.

[12]

Hauke Egermann, Mary Elizabeth Sutherland, Oliver Grewe, Frederik Nagel, Reinhard Kopiez, and Eckart Altenmüller. 2011. Does music listening in a social context alter experience? A physiological and psychological perspective on emotion. Musicae Scientiae, Vol. 15, 3 (2011), 307--323.

[13]

Gavan J Fitzsimons and Donald R Lehmann. 2004. Reactance to recommendations: When unsolicited advice yields contrary responses. Marketing Science, Vol. 23, 1 (2004), 82--94.

[14]

Alinka E Greasley and Alexandra Lamont. 2011. Exploring engagement with music in everyday life using experience sampling methodology. Musicae Scientiae, Vol. 15, 1 (2011), 45--71.

[15]

Fabian Greb, Wolff Schlotz, and Jochen Steffens. 2016. Situational and dispositional influences on the functions of music listening. (2016).

[16]

Fabian Greb, Wolff Schlotz, and Jochen Steffens. 2017. Personal and situational influences on the functions of music listening. Psychology of Music (2017), 763--794.

[17]

Chen He, 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

[18]

Andreas Heye and Alexandra Lamont. 2010. Mobile listening situations in everyday life: The use of MP3 players while travelling. Musicae Scientiae, Vol. 14, 1 (2010), 95--120.

[19]

Yucheng Jin, Karsten Seipp, Erik Duval, and Katrien Verbert. 2016. Go with the flow: effects of transparency and user control on targeted advertising using flow charts. In Proc. of AVI '16. ACM, 68--75.

Digital Library

[20]

Yucheng Jin, Nava Tintarev, and Katrien Verbert. 2018a. Effects of individual traits on diversity-aware music recommender user interfaces. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization . ACM, 291--299.

Digital Library

[21]

Yucheng Jin, Nava Tintarev, and Katrien Verbert. 2018b. Effects of personal characteristics on music recommender systems with different levels of controllability. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, 13--21.

Digital Library

[22]

Patrik N Juslin, Simon Liljeström, Daniel V"astfj"all, Goncc alo Barradas, and Ana Silva. 2008. An experience sampling study of emotional reactions to music: listener, music, and situation. Emotion, Vol. 8, 5 (2008), 668.

[23]

Matthew C Keller, Barbara L Fredrickson, Oscar Ybarra, Stéphane Côté, Kareem Johnson, Joe Mikels, Anne Conway, and Tor Wager. 2005. A warm heart and a clear head: The contingent effects of weather on mood and cognition. Psychological science, Vol. 16, 9 (2005), 724--731.

[24]

Bart P Knijnenburg, Martijn C Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. UMUAI, Vol. 22, 4--5 (2012), 441--504.

Digital Library

[25]

Stefan Koelsch. 2009. A neuroscientific perspective on music therapy. Annals of the New York Academy of Sciences, Vol. 1169, 1 (2009), 374--384.

[26]

Sherrie YX Komiak and Izak Benbasat. 2006. The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly (2006), 941--960.

Digital Library

[27]

Joseph A Konstan and John Riedl. 2012. Recommender systems: from algorithms to user experience. UMUAI'12, Vol. 22, 1 (2012), 101--123.

Digital Library

[28]

Amanda E Krause, Adrian C North, and Lauren Y Hewitt. 2016. The role of location in everyday experiences of music. Psychology of Popular Media Culture, Vol. 5, 3 (2016), 232.

[29]

Simon Liljeström, Patrik N Juslin, and Daniel V"astfj"all. 2013. Experimental evidence of the roles of music choice, social context, and listener personality in emotional reactions to music. Psychology of Music, Vol. 41, 5 (2013), 579--599.

[30]

Kevin McCarthy, Yasser Salem, and Barry Smyth. 2010. Experience-based critiquing: Reusing critiquing experiences to improve conversational recommendation. In Proc. of ICCBR'10. Springer, 480--494.

Digital Library

[31]

Martijn Millecamp, Nyi Nyi Htun, Cristina Conati, and Katrien Verbert. 2019. To explain or not to explain: the effects of personal characteristics when explaining music recommendations. Procs. of IUI, 1--12.

[32]

Martijn Millecamp, Nyi Nyi Htun, Yucheng Jin, and Katrien Verbert. 2018. Controlling Spotify recommendations: effects of personal characteristics on music recommender user Interfaces. In Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. ACM, 101--109.

Digital Library

[33]

Adrian C North and David J Hargreaves. 1996. Situational influences on reported musical preference. Psychomusicology: A Journal of Research in Music Cognition, Vol. 15, 1--2 (1996), 30.

[34]

Adrian C North, David J Hargreaves, and Jon J Hargreaves. 2004. Uses of music in everyday life. Music Perception: An Interdisciplinary Journal, Vol. 22, 1 (2004), 41--77.

[35]

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

Digital Library

[36]

Rafael R Padovani, Lucas N Ferreira, and Levi HS Lelis. 2017. Bardo: Emotion-Based Music Recommendation for Tabletop Role-Playing Games. (2017).

[37]

Denis Parra and Peter Brusilovsky. 2015. User-controllable personalization: a case study with SetFusion. IJHCS'15, Vol. 78 (2015), 43--67.

Digital Library

[38]

Shabbir A Rana and Adrian C North. 2007. The role of music in everyday life among Pakistanis. Music Perception: An Interdisciplinary Journal, Vol. 25, 1 (2007), 59--73.

[39]

Alan Said, Shlomo Berkovsky, and Ernesto W De Luca. 2010. Putting things in context: Challenge on context-aware movie recommendation. In Proceedings of the Workshop on Context-Aware Movie Recommendation. ACM, 2--6.

Digital Library

[40]

Yuri Saito and Takayuki Itoh. 2011. MusiCube: a visual music recommendation system featuring interactive evolutionary computing. In Proc. of VINCI'11. ACM, 5.

Digital Library

[41]

Jeffrey L Sanders and Mary S Brizzolara. 1982. Relationships between weather and mood. Journal of General Psychology (1982).

[42]

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.

[43]

John A Sloboda, Susan A O'Neill, and Antonia Ivaldi. 2001. Functions of music in everyday life: An exploratory study using the Experience Sampling Method. Musicae scientiae, Vol. 5, 1 (2001), 9--32.

[44]

Valerie N Stratton and Annette H Zalanowski. 1991. The effects of music and cognition on mood. Psychology of Music, Vol. 19, 2 (1991), 121--127.

[45]

Mark Tarrant, Adrian C North, and David J Hargreaves. 2000. English and American adolescents' reasons for listening to music. Psychology of Music, Vol. 28, 2 (2000), 166--173.

[46]

Eliot Van Buskirk. 2017. Spotify, Accuweather Reveal How Weather Affects Music Listening . https://insights.spotify.com/us/2017/02/07/spotify-accuweather-music-and-weather/

[47]

Mark Van Setten, Stanislav Pokraev, and Johan Koolwaaij. 2004. Context-aware recommendations in the mobile tourist application COMPASS. In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems. Springer, 235--244.

[48]

Katrien Verbert, Nikos Manouselis, Xavier Ochoa, Martin Wolpers, Hendrik Drachsler, Ivana Bosnic, and Erik Duval. 2012. Context-aware recommender systems for learning: a survey and future challenges. IEEE Transactions on Learning Technologies, Vol. 5, 4 (2012), 318--335.

Digital Library

[49]

Sergey Volokhin and Eugene Agichtein. 2018. Understanding Music Listening Intents During Daily Activities with Implications for Contextual Music Recommendation. In Proceedings of the 2018 Conference on Human Information Interaction&Retrieval. ACM, 313--316.

Digital Library

[50]

Xinxi Wang, David Rosenblum, and Ye Wang. 2012. Context-aware mobile music recommendation for daily activities. In Proceedings of the 20th ACM international conference on Multimedia. ACM, 99--108.

Digital Library

[51]

Hongzhi Yin, Bin Cui, Ling Chen, Zhiting Hu, and Zi Huang. 2014. A temporal context-aware model for user behavior modeling in social media systems. In Procs. of SIGMOD. ACM, 1543--1554.

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
https://doi.org/10.1145/3652891
Grigorev VLi JMa WHe ZZhang MLiu YYan MZhang J(2024)SiTunes: A Situational Music Recommendation Dataset with Physiological and Psychological SignalsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638343(417-421)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638343
Oh JKim WKim SIm HLee S(2024)Better to Ask Than Assume: Proactive Voice Assistants’ Communication Strategies That Respect User Agency in a Smart Home EnvironmentProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642193(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642193
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Index Terms

ContextPlay: Evaluating User Control for Context-Aware Music Recommendation
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI
    2. HCI design and evaluation methods
      1. User studies
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Personalization

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cover image ACM Conferences

UMAP '19: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

June 2019

377 pages

ISBN:9781450360210

DOI:10.1145/3320435

General Chairs:
George Angelos Papadopoulos
University of Cyprus, Cyprus
,
George Samaras
University of Cyprus, Cyprus
,
Stephan Weibelzahl
PFH Private University of Applied Sciences Göttingen, Germany
,
Program Chairs:
Dietmar Jannach
Alpen-Adria-Universität Klagenfurt, Austria
,
Olga C. Santos
aDeNu Research Group - UNED, Spain

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Published: 07 June 2019

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Onderzoeksraad, KU Leuven

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UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization

June 9 - 12, 2019

Larnaca, Cyprus

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UMAP '19 Paper Acceptance Rate 30 of 122 submissions, 25%;

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
Grigorev VLi JMa WHe ZZhang MLiu YYan MZhang J(2024)SiTunes: A Situational Music Recommendation Dataset with Physiological and Psychological SignalsProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638343(417-421)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638343
Oh JKim WKim SIm HLee S(2024)Better to Ask Than Assume: Proactive Voice Assistants’ Communication Strategies That Respect User Agency in a Smart Home EnvironmentProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642193(1-17)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642193
Amiri BShahverdi NHaddadi AGhahremani Y(2024)Beyond the Trends: Evolution and Future Directions in Music Recommender Systems ResearchIEEE Access10.1109/ACCESS.2024.338668412(51500-51522)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3386684
Yun WJian LYanlong M(2023)A Hybrid Music Recommendation Model Based on Personalized Measurement and Game TheoryChinese Journal of Electronics10.23919/cje.2021.00.17232:6(1319-1328)Online publication date: Nov-2023
https://doi.org/10.23919/cje.2021.00.172
Kleć MWieczorkowska ASzklanny KStrus W(2023)Beyond the Big Five personality traits for music recommendation systemsEURASIP Journal on Audio, Speech, and Music Processing10.1186/s13636-022-00269-02023:1Online publication date: 19-Jan-2023
https://doi.org/10.1186/s13636-022-00269-0
Assuncao WPrates RZaina L(2023)From User Context to Tailored Playlists: A User Centered Approach to Improve Music Recommendation SystemProceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems10.1145/3638067.3638084(1-11)Online publication date: 16-Oct-2023
https://dl.acm.org/doi/10.1145/3638067.3638084
Jin YChen LCai WZhao X(2023)CRS-Que: A User-centric Evaluation Framework for Conversational Recommender SystemsACM Transactions on Recommender Systems10.1145/36315342:1(1-34)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1145/3631534
Assunção WPrates RZaina L(2023)Managing Cold-Start Issues in Music Recommendation Systems: An Approach Based on User ExperienceCompanion Proceedings of the 2023 ACM SIGCHI Symposium on Engineering Interactive Computing Systems10.1145/3596454.3597180(31-37)Online publication date: 27-Jun-2023
https://dl.acm.org/doi/10.1145/3596454.3597180
Jin YCai WChen LDai YJiang T(2023)Understanding Disclosure and Support for Youth Mental Health in Social Music CommunitiesProceedings of the ACM on Human-Computer Interaction10.1145/35796297:CSCW1(1-32)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579629
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