Reading News with a Purpose

Sullivan, Emily; Bountouridis, Dimitrios; Harambam, Jaron; Najafian, Sharareh; Löecherbach, Felicia; Makhortykh, Mykola; Kelen, Domokos; Wilkinson, Daricia; Graus, David; Tintarev, Nava

doi:10.1145/3314183.3323456

Cited by 27 publications

(22 citation statements)

References 34 publications

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“…The rise of distrust and skepticism related to the collection and use of personal data, and privacy concerns in general has led to an increased interest in transparency of blackbox user models, used to provide recommendations [27]. Many researchers stressed the importance of enabling transparency by opening, scrutinizing, and explaining the black box user profiles, that serve as input for the RS.…”

Section: Explainable Recommender Systemsmentioning

confidence: 99%

“…Many researchers stressed the importance of enabling transparency by opening, scrutinizing, and explaining the black box user profiles, that serve as input for the RS. This can help users become aware of their interests used for the recommendations [28], build a more accurate mental model of the system [26], detect wrong assumptions made by the system [28], contribute to scrutability, allowing users to provide explicit feedback on their generated user profiles [6], help detect biases which is crucial to producing fair recommendations, thus leading to increased trust in the system [26], and facilitate users' self-actualization [6,27].…”

Section: Explainable Recommender Systemsmentioning

confidence: 99%

“…Explaining user models goes beyond just exposing and manipulating the user model to provide concrete explanations of how the user model was inferred. Only few works followed this approach and provided explanations of the user model to make the RS transparent [24][25][26][27]40].…”

Section: Explainable Recommender Systemsmentioning

confidence: 99%

“…These include transparency to help users better understand the underlying mechanisms of the RS, scrutability to allow users to provide explicit feedback on the constructed user profiles, and self-actualization to support users in understanding and exploring their personal preferences. Likewise, Sullivan et al [27] focused on explaining the user profile for self-actualization in the news domain. They presented a conceptual framework that defines goals of explanations, based on three layers that enable users to answer different questions depending on different goals, namely, transparency, contextualization, and self-actualization.…”

Section: Computer Science Perspectivementioning

confidence: 99%

“…Increase diversity gradually Not connect users to completely unrelated items, but gradually increase diversity, to slowly move out of their epistemic bubble [27] Visualize underdeveloped parts of a user model…”

Section: Mechanism Description Referencesmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Explainable Recommender Systemsmentioning

confidence: 99%

Section: Explainable Recommender Systemsmentioning

confidence: 99%

Section: Explainable Recommender Systemsmentioning

confidence: 99%

Section: Computer Science Perspectivementioning

confidence: 99%

Section: Mechanism Description Referencesmentioning

confidence: 99%

See 3 more Smart Citations

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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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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A survey on effects of adding explanations to recommender systems

Vultureanu‐Albişi

Bǎdicǎ

2022

Concurrency and Computation

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Explainable recommendations become essential when we need to improve the performance of recommendations and to increase user confidence. Explanations are effective when end users can build a complete and correct mental representation of the inferential process of a recommender system. This paper presents our view on the background regarding the implications of explainability applied to recommender systems. Our work contributes to the better understanding of the concept of explainable recommendation and it offers a broader picture of the development of further research in this field. Additionally, we contribute by providing a better understanding of the concept of human-centered evaluation of explainable recommender systems.

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Reading News with a Purpose

Abstract: Reading news with a purpose Explaining user profiles for self-actualization

Cited by 27 publications

References 34 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

Measuring the benefit of increased transparency and control in news recommendation

A survey on effects of adding explanations to recommender systems

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