research-article

TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems

Authors:

Savvas Petridis,

Nediyana Daskalova,

Sarah Mennicken,

Jennifer ThomAuthors Info & Claims

IUI '22: Proceedings of the 27th International Conference on Intelligent User Interfaces

Pages 120 - 133

https://doi.org/10.1145/3490099.3511156

Published: 22 March 2022 Publication History

Abstract

Recommender systems are ubiquitous and influence the information we consume daily by helping us navigate vast catalogs of information like music databases. However, their linear approach of surfacing content in ranked lists limits their ability to help us grow and understand our personal preferences. In this paper, we study how we can better support users in exploring a novel space, specifically focusing on music genres. Informed by interviews with expert music listeners, we developed TastePaths: an interactive web tool that helps users explore an overview of the genre-space via a graph of connected artists. We conducted a comparative user study with 16 participants where each of them used a personalized version of TastePaths (built with a set of artists the user listens to frequently) and a non-personalized one (based on a set of the most popular artists in a genre). We find that participants employed various strategies to explore the space. Overall, they greatly preferred the personalized version as it helped anchor their exploration and provided recommendations that were more compatible with their personal taste. In addition to that, TastePaths helped participants specify and articulate their interest in the genre and gave them a better understanding of the system’s organization of music. Based on our findings, we discuss opportunities and challenges for incorporating more control and expressive feedback in recommendation systems to help users explore spaces beyond their immediate interests and improve these systems’ underlying algorithms.

References

[1]

Fernando Amat, Ashok Chandrashekar, Tony Jebara, and Justin Basilico. 2018. Artwork Personalization at Netflix. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 487–488. https://doi.org/10.1145/3240323.3241729

Digital Library

[2]

Xavier Amatriain, Josep M. Pujol, Nava Tintarev, and Nuria Oliver. 2009. Rate It Again: Increasing Recommendation Accuracy by User Re-Rating. In Proceedings of the Third ACM Conference on Recommender Systems (New York, New York, USA) (RecSys ’09). Association for Computing Machinery, New York, NY, USA, 173–180. https://doi.org/10.1145/1639714.1639744

Digital Library

[3]

Ivana Andjelkovic, Denis Parra, and John O’Donovan. 2019. Moodplay: Interactive music recommendation based on Artists’ mood similarity. International Journal of Human-Computer Studies 121 (2019), 142–159. https://doi.org/10.1016/j.ijhcs.2018.04.004 Advances in Computer-Human Interaction for Recommender Systems.

[4]

Eric P. S. Baumer, Rui Sun, and Peter Schaedler. 2018. Departing and Returning: Sense of Agency as an Organizing Concept for Understanding Social Media Non/Use Transitions. Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 23 (Nov. 2018), 19 pages. https://doi.org/10.1145/3274292

Digital Library

[5]

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008, 10(2008), P10008.

[6]

Geoffray Bonnin and Dietmar Jannach. 2014. Automated Generation of Music Playlists: Survey and Experiments. ACM Comput. Surv. 47, 2, Article 26 (Nov. 2014), 35 pages. https://doi.org/10.1145/2652481

Digital Library

[7]

Svetlin Bostandjiev, John O’Donovan, and Tobias Höllerer. 2012. TasteWeights: A Visual Interactive Hybrid Recommender System. In Proceedings of the Sixth ACM Conference on Recommender Systems (Dublin, Ireland) (RecSys ’12). Association for Computing Machinery, New York, NY, USA, 35–42. https://doi.org/10.1145/2365952.2365964

Digital Library

[8]

Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 Data-Driven Documents. IEEE Transactions on Visualization and Computer Graphics 17, 12 (Dec. 2011), 2301–2309. https://doi.org/10.1109/TVCG.2011.185

Digital Library

[9]

Ulrik Brandes. 2001. A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology 25, 2 (2001), 163–177. https://doi.org/10.1080/0022250X.2001.9990249 arXiv:https://doi.org/10.1080/0022250X.2001.9990249

[10]

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

[11]

Wanling Cai, Yucheng Jin, and Li Chen. 2021. Critiquing for Music Exploration in Conversational Recommender Systems. In 26th International Conference on Intelligent User Interfaces (College Station, TX, USA) (IUI ’21). Association for Computing Machinery, New York, NY, USA, 480–490. https://doi.org/10.1145/3397481.3450657

Digital Library

[12]

Joseph Chee Chang, Nathan Hahn, Adam Perer, and Aniket Kittur. 2019. SearchLens: Composing and Capturing Complex User Interests for Exploratory Search. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 498–509. https://doi.org/10.1145/3301275.3302321

Digital Library

[13]

Minsuk Chang, Leonore V. Guillain, Hyeungshik Jung, Vivian M. Hare, Juho Kim, and Maneesh Agrawala. 2018. RecipeScape: An Interactive Tool for Analyzing Cooking Instructions at Scale. Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3174025

Digital Library

[14]

Duen Horng Chau, Aniket Kittur, Jason I. Hong, and Christos Faloutsos. 2011. Apolo: Making Sense of Large Network Data by Combining Rich User Interaction and Machine Learning. Association for Computing Machinery, New York, NY, USA, 167–176. https://doi.org/10.1145/1978942.1978967

Digital Library

[15]

Paolo Cremonesi, Franca Garzottto, and Roberto Turrin. 2012. User Effort vs. Accuracy in Rating-Based Elicitation. In Proceedings of the Sixth ACM Conference on Recommender Systems (Dublin, Ireland) (RecSys ’12). Association for Computing Machinery, New York, NY, USA, 27–34. https://doi.org/10.1145/2365952.2365963

Digital Library

[16]

Douglass R. Cutting, David R. Karger, Jan O. Pedersen, and John W. Tukey. 2017. Scatter/Gather: A Cluster-Based Approach to Browsing Large Document Collections. SIGIR Forum 51, 2 (Aug. 2017), 148–159. https://doi.org/10.1145/3130348.3130362

Digital Library

[17]

Liam Delaney and Leonhard K. Lades. 2017. Present Bias and Everyday Self-Control Failures: A Day Reconstruction Study. Journal of Behavioral Decision Making 30, 5 (2017), 1157–1167. https://doi.org/10.1002/bdm.2031 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/bdm.2031

[18]

Marc JMH Delsing, Tom FM Ter Bogt, Rutger CME Engels, and Wim HJ Meeus. 2008. Adolescents’ music preferences and personality characteristics. European Journal of Personality: Published for the European Association of Personality Psychology 22, 2 (2008), 109–130.

[19]

John Frow. 2014. Genre. Routledge.

[20]

Jean Garcia-Gathright, Brian St. Thomas, Christine Hosey, Zahra Nazari, and Fernando Diaz. 2018. Understanding and Evaluating User Satisfaction with Music Discovery. In The 41st International ACM SIGIR Conference on Researc & Development in Information Retrieval (Ann Arbor, MI, USA) (SIGIR ’18). Association for Computing Machinery, New York, NY, USA, 55–64. https://doi.org/10.1145/3209978.3210049

Digital Library

[21]

Carlos A. Gomez-Uribe and Neil Hunt. 2016. The Netflix Recommender System: Algorithms, Business Value, and Innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (Dec. 2016), 19 pages. https://doi.org/10.1145/2843948

Digital Library

[22]

F. Maxwell Harper, Xin Li, Yan Chen, and Joseph A. Konstan. 2005. An Economic Model of User Rating in an Online Recommender System. In User Modeling 2005, Liliana Ardissono, Paul Brna, and Antonija Mitrovic (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 307–316.

[23]

Christine Hosey, Lara Vujović, Brian St. Thomas, Jean Garcia-Gathright, and Jennifer Thom. 2019. Just Give Me What I Want: How People Use and Evaluate Music Search. Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/3290605.3300529

Digital Library

[24]

Frank K Hwang and Dana S Richards. 1992. Steiner tree problems. Networks 22, 1 (1992), 55–89.

[25]

Dietmar Jannach and Gediminas Adomavicius. 2016. Recommendations with a Purpose. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 7–10. https://doi.org/10.1145/2959100.2959186

Digital Library

[26]

Mohsen Kamalzadeh, Christoph Kralj, Torsten Möller, and Michael Sedlmair. 2016. TagFlip: Active Mobile Music Discovery with Social Tags. In Proceedings of the 21st International Conference on Intelligent User Interfaces (Sonoma, California, USA) (IUI ’16). Association for Computing Machinery, New York, NY, USA, 19–30. https://doi.org/10.1145/2856767.2856780

Digital Library

[27]

Bart P. Knijnenburg, Saadhika Sivakumar, and Daricia Wilkinson. 2016. Recommender Systems for Self-Actualization. In Proceedings of the 10th ACM Conference on Recommender Systems (Boston, Massachusetts, USA) (RecSys ’16). Association for Computing Machinery, New York, NY, USA, 11–14. https://doi.org/10.1145/2959100.2959189

Digital Library

[28]

Bart P. Knijnenburg and Martijn C. Willemsen. 2015. Evaluating Recommender Systems with User Experiments. Springer US, Boston, MA, 309–352. https://doi.org/10.1007/978-1-4899-7637-6_9

[29]

Bart P Knijnenburg, Martijn C Willemsen, Zeno Gantner, Hakan Soncu, and Chris Newell. 2012. Explaining the user experience of recommender systems. User Modeling and User-Adapted Interaction 22, 4 (2012), 441–504. https://doi.org/10.1007/s11257-011-9118-4

Digital Library

[30]

Joseph A Konstan and John Riedl. 2012. Recommender systems: from algorithms to user experience. User modeling and user-adapted interaction 22, 1 (2012), 101–123. https://doi.org/10.1007/s11257-011-9112-x

Digital Library

[31]

Johannes Kunkel, Benedikt Loepp, and Jürgen Ziegler. 2017. A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering. In Proceedings of the 22nd International Conference on Intelligent User Interfaces (Limassol, Cyprus) (IUI ’17). Association for Computing Machinery, New York, NY, USA, 3–15. https://doi.org/10.1145/3025171.3025189

Digital Library

[32]

Johannes Kunkel, Claudia Schwenger, and Jürgen Ziegler. 2020. NewsViz: Depicting and Controlling Preference Profiles Using Interactive Treemaps in News Recommender Systems. In Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization (Genoa, Italy) (UMAP ’20). Association for Computing Machinery, New York, NY, USA, 126–135. https://doi.org/10.1145/3340631.3394869

Digital Library

[33]

Audrey Laplante and J. Stephen Downie. 2011. The utilitarian and hedonic outcomes of music information-seeking in everyday life. Library & Information Science Research 33, 3 (2011), 202–210. https://doi.org/10.1016/j.lisr.2010.11.002

[34]

Jin Ha Lee, Hyerim Cho, and Yea-Seul Kim. 2016. Users’ music information needs and behaviors: Design implications for music information retrieval systems. Journal of the Association for Information Science and Technology 67, 6(2016), 1301–1330. https://doi.org/10.1002/asi.23471 arXiv:https://asistdl.onlinelibrary.wiley.com/doi/pdf/10.1002/asi.23471

Digital Library

[35]

Jin Ha Lee and Nichole Maiman Waterman. 2012. Understanding User Requirements for Music Information Services. In ISMIR. Citeseer, 253–258.

[36]

Yu Liang and Martijn C. Willemsen. 2019. Personalized Recommendations for Music Genre Exploration. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (Larnaca, Cyprus) (UMAP ’19). Association for Computing Machinery, New York, NY, USA, 276–284. https://doi.org/10.1145/3320435.3320455

Digital Library

[37]

Yu Liang and Martijn C. Willemsen. 2021. Interactive Music Genre Exploration with Visualization and Mood Control. In 26th International Conference on Intelligent User Interfaces (College Station, TX, USA) (IUI ’21). Association for Computing Machinery, New York, NY, USA, 175–185. https://doi.org/10.1145/3397481.3450700

Digital Library

[38]

Yu Liang and Martijn C Willemsen. 2021. The role of preference consistency, defaults and musical expertise in users’ exploration behavior in a genre exploration recommender. In Fifteenth ACM Conference on Recommender Systems. 230–240.

Digital Library

[39]

Kai Lukoff, Ulrik Lyngs, Himanshu Zade, J. Vera Liao, James Choi, Kaiyue Fan, Sean A. Munson, and Alexis Hiniker. 2021. How the Design of YouTube Influences User Sense of Agency. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3411764.3445467

Digital Library

[40]

James McInerney, Benjamin Lacker, Samantha Hansen, Karl Higley, Hugues Bouchard, Alois Gruson, and Rishabh Mehrotra. 2018. Explore, Exploit, and Explain: Personalizing Explainable Recommendations with Bandits. In Proceedings of the 12th ACM Conference on Recommender Systems(RecSys ’18). 31–39.

Digital Library

[41]

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. In Proceedings of the 24th International Conference on Intelligent User Interfaces (Marina del Ray, California) (IUI ’19). Association for Computing Machinery, New York, NY, USA, 397–407. https://doi.org/10.1145/3301275.3302313

Digital Library

[42]

MusicAlly. 2021. IFPI report reveals 7.4% growth in global recorded music revenues. https://musically.com/2021/03/23/ifpi-report-reveals-7-4-growth-in-global-recorded-music-revenues/

[43]

Judith Möller, Damian Trilling, Natali Helberger, and Bram van Es. 2018. Do not blame it on the algorithm: an empirical assessment of multiple recommender systems and their impact on content diversity. Information, Communication & Society 21, 7 (2018), 959–977. https://doi.org/10.1080/1369118X.2018.1444076 arXiv:https://doi.org/10.1080/1369118X.2018.1444076

[44]

Sayooran Nagulendra and Julita Vassileva. 2014. Understanding and Controlling the Filter Bubble through Interactive Visualization: A User Study. In Proceedings of the 25th ACM Conference on Hypertext and Social Media (Santiago, Chile) (HT ’14). Association for Computing Machinery, New York, NY, USA, 107–115. https://doi.org/10.1145/2631775.2631811

Digital Library

[45]

Mark EJ Newman. 2008. The mathematics of networks. The new palgrave encyclopedia of economics 2, 2008 (2008), 1–12.

[46]

Tien T. Nguyen, Pik-Mai Hui, F. Maxwell Harper, Loren Terveen, and Joseph A. Konstan. 2014. Exploring the Filter Bubble: The Effect of Using Recommender Systems on Content Diversity. In Proceedings of the 23rd International Conference on World Wide Web (Seoul, Korea) (WWW ’14). Association for Computing Machinery, New York, NY, USA, 677–686. https://doi.org/10.1145/2566486.2568012

Digital Library

[47]

Eli Pariser. 2011. The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.

Digital Library

[48]

Peter Pirolli and Stuart Card. 2005. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proceedings of international conference on intelligence analysis, Vol. 5. McLean, VA, USA, 2–4.

[49]

Pearl Pu, Li Chen, and Rong Hu. 2011. A User-Centric Evaluation Framework for Recommender Systems. In Proceedings of the Fifth ACM Conference on Recommender Systems (Chicago, Illinois, USA) (RecSys ’11). Association for Computing Machinery, New York, NY, USA, 157–164. https://doi.org/10.1145/2043932.2043962

Digital Library

[50]

Anand Rajaraman and Jeffrey David Ullman. 2011. Mining of massive datasets. Cambridge University Press.

[51]

Even Ruud. 1997. Music and identity. Nordisk Tidsskrift for Musikkterapi 6, 1 (1997), 3–13. https://doi.org/10.1080/08098139709477889 arXiv:https://doi.org/10.1080/08098139709477889

[52]

Johnny Saldaña. 2015. The coding manual for qualitative researchers. Sage.

[53]

E. Isaac Sparling and Shilad Sen. 2011. Rating: How Difficult is It?. In Proceedings of the Fifth ACM Conference on Recommender Systems (Chicago, Illinois, USA) (RecSys ’11). Association for Computing Machinery, New York, NY, USA, 149–156. https://doi.org/10.1145/2043932.2043961

Digital Library

[54]

Maria Taramigkou, Efthimios Bothos, Konstantinos Christidis, Dimitris Apostolou, and Gregoris Mentzas. 2013. Escape the Bubble: Guided Exploration of Music Preferences for Serendipity and Novelty. In Proceedings of the 7th ACM Conference on Recommender Systems (Hong Kong, China) (RecSys ’13). Association for Computing Machinery, New York, NY, USA, 335–338. https://doi.org/10.1145/2507157.2507223

Digital Library

[55]

Nava Tintarev, Shahin Rostami, and Barry Smyth. 2018. Knowing the Unknown: Visualising Consumption Blind-Spots in Recommender Systems. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing (Pau, France) (SAC ’18). Association for Computing Machinery, New York, NY, USA, 1396–1399. https://doi.org/10.1145/3167132.3167419

Digital Library

[56]

Pontus Wärnestål. 2005. User evaluation of a conversational recommender system. In Proceedings of the 4th Workshop on Knowledge and Reasoning in Practical Dialogue Systems.

[57]

Michael Yeomans, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg. 2019. Making sense of recommendations. Journal of Behavioral Decision Making 32, 4 (2019), 403–414.

[58]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv. 52, 1, Article 5 (Feb. 2019), 38 pages. https://doi.org/10.1145/3285029

Digital Library

[59]

Nanxuan Zhao, Nam Wook Kim, Laura Mariah Herman, Hanspeter Pfister, Rynson W.H. Lau, Jose Echevarria, and Zoya Bylinskii. 2020. ICONATE: Automatic Compound Icon Generation and Ideation. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3313831.3376618

Digital Library

[60]

Frederik Zuiderveen Borgesius, Damian Trilling, Judith Möller, Balázs Bodó, Claes H De Vreese, and Natali Helberger. 2016. Should we worry about filter bubbles?Internet Policy Review. Journal on Internet Regulation 5, 1 (2016).

Cited By

Petridis SWedin BWexler JPushkarna MDonsbach AGoyal NCai CTerry M(2024)ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into PrinciplesProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645144(853-868)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645144
Ziegler JLoepp B(2024)Interactive Recommendation SystemsHandbook of Human Computer Interaction10.1007/978-3-319-27648-9_54-1(1-29)Online publication date: 11-Feb-2024
https://doi.org/10.1007/978-3-319-27648-9_54-1
Bartels ESmits ADetweiler Cvan der Stappen Evan Rossen SShayan SPott KCardona KZiegler Jvan Turnhout K(2024)Exploring Categorizations of Algorithmic Affordances in Graphical User Interfaces of Recommender SystemsDesign for Equality and Justice10.1007/978-3-031-61698-3_16(173-184)Online publication date: 9-Jul-2024
https://doi.org/10.1007/978-3-031-61698-3_16
Show More Cited By

Index Terms

TastePaths: Enabling Deeper Exploration and Understanding of Personal Preferences in Recommender Systems

Index terms have been assigned to the content through auto-classification.

Recommendations

Music/lyrics composition system considering user's image and music genre
SMC'09: Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics

This paper proposes a music/lyrics composition system consisting of two sections, a lyric composing section and a music composing section, which considers user's image of a song and music genre. First of all, a user has an image of music/lyrics to ...
Using adaptive avatars for visualizing recent music listening history and supporting music discovery
ACE '14: Proceedings of the 11th Conference on Advances in Computer Entertainment Technology

This paper studies the idea of using adaptive avatars for visualizing recent music listening, supporting music discovery, and encouraging social interaction. In the evaluated prototype the avatar responds to the user's music listening by changing its ...
Interactive Music Genre Exploration with Visualization and Mood Control
IUI '21: Proceedings of the 26th International Conference on Intelligent User Interfaces

Recommender systems can be used to help users discover novel items and explore new tastes, for example in music genre exploration. However, little work has studied how to improve users’ understandability and acceptance of the novel items as well as ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IUI '22: Proceedings of the 27th International Conference on Intelligent User Interfaces

March 2022

888 pages

ISBN:9781450391443

DOI:10.1145/3490099

Copyright © 2022 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].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

IUI '22

Sponsor:

IUI '22: 27th International Conference on Intelligent User Interfaces

March 22 - 25, 2022

Helsinki, Finland

Acceptance Rates

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

Upcoming Conference

IUI '25

Sponsor:
sigai
sigai

30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

Cagliari , Italy

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
623
Total Downloads

Downloads (Last 12 months)118
Downloads (Last 6 weeks)11

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Petridis SWedin BWexler JPushkarna MDonsbach AGoyal NCai CTerry M(2024)ConstitutionMaker: Interactively Critiquing Large Language Models by Converting Feedback into PrinciplesProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645144(853-868)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645144
Ziegler JLoepp B(2024)Interactive Recommendation SystemsHandbook of Human Computer Interaction10.1007/978-3-319-27648-9_54-1(1-29)Online publication date: 11-Feb-2024
https://doi.org/10.1007/978-3-319-27648-9_54-1
Bartels ESmits ADetweiler Cvan der Stappen Evan Rossen SShayan SPott KCardona KZiegler Jvan Turnhout K(2024)Exploring Categorizations of Algorithmic Affordances in Graphical User Interfaces of Recommender SystemsDesign for Equality and Justice10.1007/978-3-031-61698-3_16(173-184)Online publication date: 9-Jul-2024
https://doi.org/10.1007/978-3-031-61698-3_16
Dinnissen KSaccardi IVredenborg MBauer C(2023)Looking at the FAccTs: Exploring Music Industry Professionals' Perspectives on Music Streaming Services and RecommendationsProceedings of the 2nd International Conference of the ACM Greek SIGCHI Chapter10.1145/3609987.3610011(1-5)Online publication date: 27-Sep-2023
https://dl.acm.org/doi/10.1145/3609987.3610011
Maccatrozzo VKuhn TCeolin DVan Ossenbruggen J(2023)The Role of Serendipity in User-Curated Music PlaylistsProceedings of the 12th Knowledge Capture Conference 202310.1145/3587259.3627552(140-147)Online publication date: 5-Dec-2023
https://dl.acm.org/doi/10.1145/3587259.3627552
Liang YPonnada ALamere PDaskalova N(2023)Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender SystemsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584032(142-155)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584032
Kunkel JZiegler J(2023)A comparative study of item space visualizations for recommender systemsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2022.102987172:COnline publication date: 1-Apr-2023
https://dl.acm.org/doi/10.1016/j.ijhcs.2022.102987
Saito TSato-Shimokawara E(2023)Music Recommender System Considering the Variations in Music Selection Criterion Using an Interactive Genetic AlgorithmComputer Information Systems and Industrial Management10.1007/978-3-031-42823-4_28(382-393)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-42823-4_28
Gabbolini GBridge D(2022)A User-Centered Investigation of Personal Music ToursProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3546776(25-34)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3546776
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten