Knowing the unknown

Tintarev, Nava; Rostami, Shahin; Smyth, Barry

doi:10.1145/3167132.3167419

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…Recognize blind spots Encourage users to identify their blind spots through visualizations. [58,59] in the user model Make users aware of blind-spots in their profile.…”

Section: Goal Description Referencesmentioning

confidence: 99%

“…As a possible mechanism to achieve the sub-goal Broaden horizons, Sullivan et al [27] proposed to "Increase diversity gradually" by making small steps outside of users' current circle of interests. The second sub-goal Recognize blind spots in the user model was extracted from [38,54,55,[58][59][60]. For instance, Tintarev et al [59] investigated how they can help users to better understand their consumption profiles through visualizations aiming at revealing to users regions of the recommendation space that are unknown to them, i.e., blind-spots.…”

Section: Explorementioning

confidence: 99%

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Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Guesmi

Chatti

Tayyar

et al. 2022

MTI

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This contribution sheds light on the potential of transparent user models for self-actualization. It discusses the development of EDUSS, a conceptual framework for self-actualization goals of transparent user modeling. Drawing from a qualitative research approach, the framework investigates self-actualization from psychology and computer science disciplines and derives a set of self-actualization goals and mechanisms. Following a human-centered design (HCD) approach, the framework was applied in an iterative process to systematically design a set of interactive visualizations to help users achieve different self-actualization goals in the scientific research domain. For this purpose, an explainable user interest model within a recommender system is utilized to provide various information on how the interest models are generated from users’ publication data. The main contributions are threefold: First, a synthesis of research on self-actualization from different domains. Second, EDUSS, a theoretically-sound self-actualization framework for transparent user modeling consisting of five main goals, namely, Explore, Develop, Understand, Scrutinize, and Socialize. Third, an instantiation of the proposed framework to effectively design interactive visualizations that can support the different self-actualization goals, following an HCD approach.

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“…Recognize blind spots Encourage users to identify their blind spots through visualizations. [58,59] in the user model Make users aware of blind-spots in their profile.…”

Section: Goal Description Referencesmentioning

confidence: 99%

Section: Explorementioning

confidence: 99%

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Guesmi

Chatti

Tayyar

et al. 2022

MTI

View full text Add to dashboard Cite

show abstract

“…In this section, we briefly summarize research that was designed to support new tastes exploration, from raising the awareness of users to explore [26,35] to supporting the exploration process towards the new tastes. Visualization can serve as a way to raise users awareness towards exploring new tastes.…”

Section: Related Work 21 Towards New Tastesmentioning

confidence: 99%

“…Visualization can serve as a way to raise users awareness towards exploring new tastes. Through visualizations, users are able to identify their blind spots [35] or understand the underlying filtering mechanisms [26], and be further motivated to explore the under-explored item space [16]. More recently, by visualizing users' profile inside an overview of the entire news domain with an interactive tree-map, the interactive news recommender system (NewsViz) proposed by Kunkel et al [18] provides users with a way to spot the over-represented news sources and raise their awareness towards potential bias.…”

Section: Related Work 21 Towards New Tastesmentioning

confidence: 99%

“…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]. From the recommender system perspective, supporting new preferences exploration could also keep users away [26] from the so called "filter bubble" issue [28], and encourage them to explore their blind spot [35].…”

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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Measuring the benefit of increased transparency and control in news recommendation

Tintarev,

Knijnenburg,

Willemsen

2024

AI Magazine

Self Cite

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Personalized news experiences powered by recommender systems permeate our lives and have the potential to influence not only our opinions, but also our decisions. At the same time, the content and viewpoints contained within news recommendations are driven by multiple factors, including both personalization and editorial selection. Explanations could help users gain a better understanding of the factors contributing to the news items selected for them to read. Indeed, recent works show that explanations are essential for users of news recommenders to understand their consumption preferences and set intentions in line with their goals, such as goals for knowledge development and increased diversity of content or viewpoints. We give examples of such works on explanation and interactive interface interventions which have been effective in influencing readers' consumption intentions and behaviors in news recommendations. However, the state‐of‐the‐art in news recommender systems currently fall short in terms of evaluating such interventions in live systems, limiting our ability to measure their true impact on user behavior and opinions. To help understand the true benefit of these interfaces, we therefore call for improving the realism of studies for news.

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Knowing the unknown

Cited by 23 publications

References 9 publications

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach

Interactive Music Genre Exploration with Visualization and Mood Control

Measuring the benefit of increased transparency and control in news recommendation

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