TagFlip

Kamalzadeh, Mohsen; Kralj, Christoph; Möller, Torsten B.; Sedlmair, Michael

doi:10.1145/2856767.2856780

Cited by 11 publications

(2 citation statements)

References 31 publications

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“…With self-reflection [13] or the increasing awareness for exploration, people gradually step into the action stage of the behavioral change process [29], willing to take actions to explore new tastes (compared to their current preferences). In music domain, to help users to discover songs in a more active way, Kamalzadeh et al proposed a novel interface called Tagflip [12], which allows users to specify the social tags of songs they would like to play next. The authors found that comparing to the Spotify interface, the proposed Tagflip was perceived more transparent and controllable.…”

Section: Related Work 21 Towards New Tastesmentioning

confidence: 99%

“…The purpose of a recommender system is not necessarily limited to help users find relevant items that fit only their long-term preferences [8]. Recently, more and more recommender systems have also been proposed to help users to discover novel items, such as novel music tracks [1,12], or support them to explore new tastes [19,34]. From the perspective of users, this type of recommender systems can serve as a way to fulfill their internal desires for seeking out new preferences [21].…”

Section: Introductionmentioning

confidence: 99%

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Interactive Music Genre Exploration with Visualization and Mood Control

Liang¹,

Willemsen

2021

26th International Conference on Intelligent User Interfaces

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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 support users to explore a new domain. In this paper, we investigate how two different visualizations and mood control influence the perceived control, informativeness and understandability of a music genre exploration tool, and further to improve the helpfulness for new music genre exploration. Specifically, we compare a bar chart visualization used by earlier work to a contour plot which allows users to compare their musical preferences with both the recommended tracks as well as the new genre. Mood control is implemented with two sliders to set a preferred mood on energy and valence features (that correlate with psychological mood dimensions). In the online user study, mood control was manipulated between subjects, and the visualizations were compared within subjects. During the study (N=102), we measured users' subjective perceptions, experiences and the interactions with the system. Our results show that the contour plot visualization is perceived more helpful to explore new genres than the bar chart visualization, as the contour plot is perceived to be more informative and understandable. Users spent significantly more time and used the mood control more in the contour plot than in the bar chart visualization. Overall, our results show that the contour plot visualization combined with mood control serves as the most helpful way for new music genre exploration, because the mood control is easier to understand and use when made transparent via an informative visualization.

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Section: Related Work 21 Towards New Tastesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Interactive Music Genre Exploration with Visualization and Mood Control

Liang¹,

Willemsen

2021

26th International Conference on Intelligent User Interfaces

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Considering emotions and contextual factors in music recommendation: a systematic literature review

Assunção

Piccolo

Zaina

2022

Multimed Tools Appl

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Effects of recommendations on the playlist creation behavior of users

Kamehkhosh

Bonnin

Jannach

2019

User Model User-Adap Inter

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The digitization of music, the emergence of online streaming platforms and mobile apps have dramatically changed the ways we consume music. Today, much of the music that we listen to is organized in some form of a playlist, and many users of modern music platforms create playlists for themselves or to share them with others. The manual creation of such playlists can however be demanding, in particular due to the huge amount of possible tracks that are available online. To help users in this task, music platforms like Spotify provide users with interactive tools for playlist creation. These tools usually recommend additional songs to include given a playlist title or some initial tracks. Interestingly, little is known so far about the effects of providing such a recommendation functionality. We therefore conducted a user study involving 270 subjects, where one half of the participants-the treatment group-were provided with automated recommendations when performing a playlist construction task. We then analyzed to what extent such recommendations are adopted by users and how they influence their choices. Our results, among other aspects, show that about two thirds of the treatment group made active use of the recommendations. Further analyses provide additional insights about the underlying reasons why users selected certain recommendations. Finally, our study also reveals that the mere presence of the recommendations impacts the choices of the participants, even in cases when none of the recommendations was actually chosen.

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TagFlip

Cited by 11 publications

References 31 publications

Interactive Music Genre Exploration with Visualization and Mood Control

Interactive Music Genre Exploration with Visualization and Mood Control

Considering emotions and contextual factors in music recommendation: a systematic literature review

Effects of recommendations on the playlist creation behavior of users

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