| 1. |
- Andersen, Kristina, 1970-, et al.
(författare)
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The Dial: Exploring Computational Strangeness
- 2016
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Ingår i: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '16). - New York, USA : ACM Press.
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Konferensbidrag (refereegranskat)abstract
- This paper describes the process of a computational ideaemerging from a process of user engagement: algorithmicrecommendations as artistic obstructions in creativework. Through a collaboration between HCI and Music InformationRetrieval, we conducted open-ended interviewswith professional makers of Electronic Dance Music. Wedescribe how the idea emerged from this process, and considerhow algorithmic recommendation systems could bere-considered as tools for artistic inspiration. We proposethe concept of a “Strangeness Dial,” which allows the gradualadjustment of the degree of desired otherness, which istested through the use of a non-functional prop and a seriesof interviews.
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| 2. |
- Knees, Peter, et al.
(författare)
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A Reproducibility Study On User-Centric Mir Researchand Why It Is Important
- 2022
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Ingår i: Proceedings of the 23rd ISMIR Conference, Bengaluru, India, December 4-8, 2022. ; , s. 764-771
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Konferensbidrag (refereegranskat)abstract
- Reproducibility of results is a central pillar of scientific work. In music information retrieval research, this is widely acknowledged and practiced by the communityby re-implementing algorithms and re-validating machine learning experiments. In this paper, we argue for an increased need to also reproduce the results and findings of user studies, including qualitative work, especially since these often lay the foundations and serve as justification for choices taken in algorithmic design and optimization criteria. As an example, we attempt to reproduce the study by Kim et al. [1] presented in the RecSys (2020) paper "Do Channels Matter? Illuminating Interpersonal Influence on Music Recommendations". By repeating this study on how interpersonal relationships can affect a user’s assessment of music recommendations on a new sample of n = 142 participants, we can largely confirm and support the validity of the original results. At the same time, we extend the analysis and also observe differences with regards to adoption rates between different channels as well as different factors that influences the adoption rate. From this specific reproducibility study, we conclude that potential cultural differences should be accounted for more explicitly in future studies and that systems development should be more explicitly connected to its intended target audience.
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| 3. |
- Knees, Peter, et al.
(författare)
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Listener awareness in music recommender systems : directions and current trends
- 2023. - 2
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Ingår i: Personalized human-computer interaction. - Oldenbourg : Walter de Gruyter. - 9783110999600 - 9783110988567 - 9783110988772 ; , s. 279-312
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Bokkapitel (refereegranskat)abstract
- Music recommender systems are a widely adopted application of personalized systems and interfaces. By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (contentbased methods), meta-data and relational data (knowledge-based methods; sometimes also considered content-based methods) or a mixture of these with other features (hybrid methods). In this chapter, we focus on the listener’s aspects of music recommender systems. We discuss different factors influencing relevance for recommendation on both the listener’s and the music’s side and categorize existing work. In more detail, we then review aspects of (i) listener background in terms of individual, i. e., personality traits and demographic characteristics, and cultural features, i. e., societal and environmental characteristics, (ii) listener context, in particular modeling dynamic properties and situational listening behavior and (iii) listener intention, in particular by studying music information behavior, i. e., how people seek, find and use music information. This is followed by a discussion of user-centric evaluation strategies for music recommender systems. We conclude the chapter with a reflection on current barriers, by pointing out current and longer-term limitations of existing approaches and outlining strategies for overcoming these.
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| 4. |
- Knees, Peter, et al.
(författare)
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User Awareness in Music Recommender Systems
- 2019
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Ingår i: Personalized human-computer interaction. - Berlin : Walter de Gruyter. - 9783110552485 - 9783110552614
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Bokkapitel (refereegranskat)abstract
- Music recommender systems are a widely adopted application of personalized systems and interfaces.By tracking the listening activity of their users and building preference profiles, a user can be given recommendations based on the preference profiles of all users (collaborative filtering), characteristics of the music listened to (content-based methods), meta-data and relational data (knowledge-based methods; sometimes also considered content-based methods) or a mixture of these with other features (hybrid methods).In this chapter, we focus on the listener's aspects of music recommender systems.We discuss different factors influencing relevance for recommendation on both the listener's and the music's side and categorize existing work. In more detail, we then review aspects of (i) listener background in terms of individual, i.e., personality traits and demographic characteristics, and cultural features, i.e., societal and environmental characteristics, (ii) listener context, in particular modeling dynamic properties and situational listening behavior, and (iii) listener intention, in particular by studying music information behavior, i.e., how people seek, find, and use music information.This is followed by a discussion of user-centric evaluation strategies for music recommender systems. We conclude the chapter with a reflection on current barriers, by pointing out current and longer-term limitations of existing approaches and outlining strategies for overcoming these.
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| 5. |
- Schedl, Markus, et al.
(författare)
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Indicators of Country Similarity in Terms of Music Taste, Cultural, and Socio-economic Factors
- 2017
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Ingår i: Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017. - : IEEE. - 9781538629383 - 9781538629369 ; , s. 308-311
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Konferensbidrag (refereegranskat)abstract
- Considering the cultural background of users is known to improve recommender systems for multimedia items. In this work, we focus on music and analyze user demographics and music listening events in a large corpus (120,000 users, 109 events) from Last.fm to investigate whether similarity between countries in terms of cultural and socio-economic factors is reflected in music taste. To this end, we propose a tag-based model to describe the music taste of a country and correlate the resulting music profiles to Hofstede’s cultural dimensions and the Quality of Government data. Spearman’s rank-order correlation and Quadratic Assignment Procedure indeed indicate statistically significant weak to medium correlations of music taste and several cultural and socio-economic factors. The results will help elaborating culture-aware models of music listeners and in turn likely yield improved music recommender systems.
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