Having tens of millions of musical works available at a listener’s fingertips requires novel recommendation and interaction techniques for music consumption. Thereby the success of a music recommender system, a system that proposes users what to explore or listen to next, depends on its ability to propose the right music, to the right user, at the right moment (i.e., in the right context). However, this task is extremely complex, as various factors influence a user’s music preferences. Amongst others, cultural aspects and characteristics (e.g., different requirements regarding diversity ofa playlist or familiarity with its music tracks) have been shown to affect music perception, preferences, and listening behavior. Calling on this, the project entitled “Fine-grained culture-aware music recommender systems” investigates how music recommender systems could and should integrate cultural aspects in order to provide better recommendations. The research findings will answer the question how music recommender systems have to be designed to reflect cultural diversity and will provide insights into cross-cultural music perception, preferences, and listening behavior. Specifically, the project will investigate the cultural requirements on music recommender systems – as concerns what listeners in different cultures expect with regard to the recommended music. Thereby, we postulate that different granularity levels of culture (e.g., individual, regional, national, or global level) have to be considered to improve music recommender systems. We hypothesize that the various cultural levels of different granularities have to be combined in a comprehensive way to transcend limitations of current music recommender systems. And we will investigate its impact on recommendation quality in cross-cultural studies with users from Austria, the United States, and Korea.
Our scientific approach comprises four methodological orientations: (i) a combination of surveys and user panels, (ii) user modeling, (iii) designing and implementing prototypes of culture-aware music recommender systems, and (iv) cross-cultural studies with users to investigate their performance. The samples will include users from the United States, Austria, and Korea; we will focus on national culture, but also consider regional cultures (e.g., urban vs. suburban vs. countryside areas). In contrast to past research in the field of culture-aware music information retrieval and recommendation, the project follows an approach that is driven by user needs and preferences. The project aims to design and implement music recommender systems that are able to meet those requirements by considering different granularity levels of cultural aspects in a comprehensive way.
2017 - 2020
Best Paper Award
(International Conference on Advances in Mobile Computing & Multimedia MoMM 2017)
Schedl, Markus & Bauer, Christine (2017). Introducing Global and Regional Mainstreaminess for Improving Personalized Music Recommendation. Proceedings of the 15th International Conference on Advances in Mobile Computing & Multimedia (MoMM 2017). 4-6 Dec, ACM, pp 74-81. DOI: 10.1145/3151848.3151849
In scope of the AI x Music Matinée at the Ars Electronica Festival 2019, Christine Bauer and Peter Knees gave a talk on music information retrieval. The video of the entire talk is available at Dorf.tv
Bauer, Christine & Schedl, Markus (2019). Global and country-specific mainstreaminess measures: Definitions, analysis, and usage for improving personalized music recommendation systems. PLOS ONE, 14(6), e0217389. DOI: 10.1371/journal.pone.0217389
Schedl, Markus & Bauer, Christine (2018). An analysis of global and regional mainstreaminess for personalized music recommender systems. Journal of Mobile Multimedia, 14(1), River Publishers, pp 95-112. DOI: 10.13052/jmm1550-4646.1415
Bauer, Christine & Ferwerda, Bruce (2020). Conformity Behavior in Group Playlist Creation. Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (CHI’20 Extended Abstracts), Honolulu, HI, USA, 25-30 April, ACM, LBW 114. DOI: 10.1145/3334480.3382942
Bauer, Christine (2019). Allowing for equal opportunities for artists in music recommendation. Proceedings of the 1st Workshop on Designing Human-Centric MIR Systems (wsHCMIR 2019), satellite event to 20th Annual Conference of the International Society for Music Information Retrieval (ISMIR 2019), Delft, The Netherlands, 2 November, pp 16-18. arXiv:1911.05395
Bauer, Christine, Schedl, Markus, Angerer, Vera, & Wegenkittl, Stefan (2019). Tastalyzer: Audiovisual exploration of urban and rural variations in music taste. Proceedings of the 27th ACM International Conference on Multimedia (ACMMM 2019), Nice, France, 21-25 October, ACM, pp 1044-1046. DOI: 10.1145/3343031.3350585
Bauer, Christine & Zangerle, Eva (2019). Leveraging Multi-Method Evaluation for Multi-Stakeholder Settings. Proceedings of the 1st Workshop on the Impact of Recommender Systems (ImpactRS 2019), part of the 13th ACM Conference on Recommender Systems (RecSys 2019), Copenhagen, Denmark, 19 September, CEUR-WS.org, Vol-2462.
Bauer, Christine & Schedl, Markus (2019). Cross-country user connections in an online social network for music. Proceedings of Proceedings of the CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI’19 Extended Abstracts), Glasgow, Scotland UK, 4-9 May, ACM. DOI: 10.1145/3290607.3312831
Bauer, Christine (2019). The Potential of the Confluence of Theoretical and Algorithmic Modeling in Music Recommendation. Proceedings of the ACM CHI 2019 Workshop on Computational Modeling in Human-Computer Interaction, Glasgow, Scotland UK, 5 May, ACM. arxiv:1911.07328
Bauer, Christine & Schedl, Markus (2019). A cross-country investigation of user connection patterns in online social networks. Proceedings of the 52nd Hawaii International Con-ference on System Sciences (HICSS 2019). Grand Wailea, Maui, HI, 8–11 January, pp 2166-2175. Handle: http://hdl.handle.net/10125/59655. DOI: 10.24251/HICSS.2019.263
Bauer, Christine & Schedl, Markus (2018). Investigating cross-country relationship between users’ social ties and music mainstreaminess. Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018). Paris, France, 23-27 September, ISMIR, pp 678-686. link to paper
Celik, Ilknur, Torre, Ilaria, Koceva, Frosina, Bauer, Christine, Zangerle, Eva, & Knijnenburg, Bart (2018). UMAP 2018 Intelligent User-Adapted Interfaces: Design and Multi-Modal Evaluation (IUadaptMe) Workshop Chairs' Welcome & Organization. Proceedings of the UMAP ’18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. Singapore, 8 July, ACM, pp 137-139. DOI: 10.1145/3213586.3226202
Schedl, Markus, Wiechert, Eelco, & Bauer, Christine (2018). The effects of real-world events on music listening behavior: an intervention time series analysis. Proceedings of the WWW ’18 Companion: The 2018 Web Conference Companion (WWW 2018). Lyon, France, 23-27 April, ACM, pp 75-76. DOI: 10.1145/3184558.3186936
Bauer, Christine & Schedl, Markus (2018). On the Importance of Considering Country-Specific Aspects on the Online-Market: An Example of Music Recommendation Considering Country-Specific Mainstream. Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS 2018). Waikoloa, Big Island, HI, 3-6 January, pp 3647-3656. Handle: http://hdl.handle.net/10125/50349. DOI: 10.24251/HICSS.2018.461
Schedl, Markus & Bauer, Christine (2017). Introducing Global and Regional Mainstreaminess for Improving Personalized Music Recommendation. Proceedings of the 15th International Conference on Advances in Mobile Computing & Multimedia (MoMM 2017). Salzburg, Austria, 4-6 December, ACM, pp 74-81. DOI: 10.1145/3151848.3151849
Best Paper Award
Schedl, Markus & Bauer, Christine (2017). Online Music Listening Culture of Kids and Adolescents: Listening Analysis and Music Recommendation Tailored to the Young. Proceedings of the 11th ACM Conference on Recommender Systems (RecSys 2017): International Workshop on Children and Recommender Systems (KidRec 2017). Como, Italy, 27 August. arxiv:1912.11564
Bauer, Christine & Schedl, Markus (2017). Introducing Surprise and Opposition by Design in Recommender Systems. Proceedings of the 25th International Conference on User Modeling, Adaptation and Personalization (UMAP 2017): 2nd Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP 2017). Bratislava, Slovakia, 9 July, pp 350-353. DOI: 10.1145/3099023.3099099
Schedl, Markus & Bauer, Christine (2017). Distance- and Rank-Based Music Mainstreaminess Measurement. Proceedings of the 25th International Conference on User Modeling, Adaptation and Personalization (UMAP 2017): 2nd Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP 2017). Bratislava, Slovakia, 9 July, pp 364-367. DOI: 10.1145/3099023.3099098
Bauer, Christine (2020). Multi-Method Evaluation: Leveraging Multiple Methods to Answer What You Were Looking For. Proceedings of the 2020 Conference on Human Information Interaction and Retrieval (CHIIR ’20). Vancouver BC, Canada, 14-18 March, pp 472-474. DOI: 10.1145/3343413.3378015
Want to know how the (national and international) public media report on the project?
Portrait of Christine Bauer in the magazine Burgenland-Mitte
The magazine Burgenland-Mitte features a portrait of Christine Bauer and explains how cultural aspects that affect music taste can be used for music recommendation.
1 April 2020, Burgenland Mitte. Die ganze Welt schaut auf sie! (in German).
The podcast Ö1 Radiokolleg (by the Austrian public broadcaster ORF) devoted for episodes to the topic of music and algorithms, and their impact on the music industry. Principal Investigator Christine Bauer is among the experts interviewed for the program. The program is called “Maschinenmusik“ and was first broadcasted in October 2019.
This project receives funding from the Austrian Science Fund (Fonds zur Förderung der wissenschaftlichen Forschung, FWF) under the Elise Richter program - an excellent program by FWF - under grant agreement No V579.