Prediction of User Demographics from Music Listening Habits

Krismayer, Thomas; Schedl, Markus; Knees, Peter; Rabiser, Rick

doi:10.1145/3095713.3095722

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…We build on our previous work on this topic -presented in [19]. In this work, we report insights that go beyond the prior findings and are based on additionally analyzing the obtained results in detail.…”

Section: Introductionmentioning

confidence: 88%

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Predicting user demographics from music listening information

Krismayer

Schedl

Knees

et al. 2018

Multimed Tools Appl

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Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed and used to improve user experience and increase revenue, e. g., through better-fitting recommendations and more targeted marketing. Analyses of digital traces typically aim to find user traits such as age, gender, and nationality to derive common preferences. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a feature modeling approach building on Term Frequency-Inverse Document Frequency (TF-IDF) for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We also compare the performance of our algorithm for different user groups and discuss possible prediction errors and how to mitigate them. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender system that can directly utilize the user attributes predicted by our algorithm to increase the quality of it's recommendations.

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Section: Introductionmentioning

confidence: 88%

“…These groups contain the users in the age intervals [6][7][8][9][10][11][12][13][14][15][16][17], [18][19][20][21], [22][23][24][25], [26][27][28][29][30], [31][32][33][34][35][36][37][38][39][40] Figure 1 shows the distribution of users in these age groups.…”

Section: Age Groupsmentioning

confidence: 99%

Predicting user demographics from music listening information

Krismayer

Schedl

Knees

et al. 2018

Multimed Tools Appl

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“…With the birth and development of mobile media and new types of smart equipment, especially smartphone mobile equipment, the quality of new media communication has increased. This has changed people’s lifestyles, and the users’ original media contact time and habits have been disrupted, while the fragmentation and dissemination of information have also cultivated new reading and audiovisual habits [ 1 ]. On the one hand, the fragmentation of listening time and content enables users to listen across time and space.…”

Section: Introductionmentioning

confidence: 99%

Effect of the Affordances of the FM New Media Communication Interface Design for Smartphones

Chen

2021

Sensors

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Smartphone equipment has promoted the widespread use of new media communication, and users have changed from being passive to actively receiving information. This has changed people’s lifestyles, and has enriched the convenience and entertainment value of knowledge acquisition. However, the new media communication systems can be too complex, and the interface design for users to interact with the new media may not be sufficiently intuitive, which causes interface usability problems. Therefore, this research study focuses on the concept of affordance and the impact of user perception pertinent to smartphone applications on frequency modulation (FM) new media interface design. The experiment is a between-subjects design using one-way ANOVA to examine three different operation modes, namely the Litchi, Himalayas and Archimedes types. The experimental data were obtained through task performance and subjective evaluation. The results indicate that: (1) Visual information presentation methods, such as viewing and deleting, affect user perception. The three operating modes revealed significant differences, with the Himalayas type taking the least amount of task performance time. (2) There were significant differences among the different operation modes, with the Himalayas type the best in terms of the users’ subjective evaluation. (3) The overall analysis of task performance and satisfaction consistently showed that the Himalayas type was better than the Litchi and Archimedes types in all aspects. (4) Smartphone user interface applications provide users with cognitive support, objects, functions, and sensory affordance, which enhance the user’s interactive experience of FM new media.

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“…In this work, we focus on the music domain (in particular, analyzing the OSN for music Last.fm (www.last.fm), with the goal to use findings in personalized (music) recommender systems. Besides social ties, many other factors influence an individual's music taste and preferences including, for instance, demographics [10] or personality traits [5]. In user modeling and recommender systems research, cultural aspects-as drivers of music taste and preferences-have only recently started gaining considerable attention.…”

Section: Introductionmentioning

confidence: 99%

Cross-country User Connections in an Online Social Network for Music

Bauer

Schedl

2019

Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems

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Social connections and cultural aspects play important roles in shaping an individual's preferences. For instance, people tend to select friends with similar music preferences. Furthermore, preferences and friending are influenced by cultural aspects. Recommender systems may benefit from these phenomena by using knowledge about the nature of social ties to better tailor recommendations to an individual. Focusing on the specifities of music preferences, we study user connections on Last.fm-an online social network for music. We identify those countries whose users are mainly connected within the same country, and those countries that are characterized by crosscountry user connections. Strong crosscountry connection pairs are typically characterized by similar cultural, historic, or linguistic backgrounds, or geographic proximity. The United States, the United Kingdom, and Russia are identified as countries having a large relative amount of user connections from other countries. Our results contribute to understanding the complexity of social ties and how they are reflected in connection behavior, and are a promising source for advancements of personalized systems.

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Prediction of User Demographics from Music Listening Habits

Cited by 7 publications

References 17 publications

Predicting user demographics from music listening information

Predicting user demographics from music listening information

Effect of the Affordances of the FM New Media Communication Interface Design for Smartphones

Cross-country User Connections in an Online Social Network for Music

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