research-article

Validation of the EDUSS Framework for Self-Actualization Based on Transparent User Models: A Qualitative Study

Authors:

Clara Siepmann,

Mohamed Amine Chatti,

Rawaa AlatrashAuthors Info & Claims

UMAP '23 Adjunct: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

Pages 229 - 238

https://doi.org/10.1145/3563359.3597379

Published: 16 June 2023 Publication History

Abstract

Self-actualization is the process of striving toward full potential and achieving higher goals in one’s life. Originally studied in psychology, this concept has been adopted by various disciplines, including recommender systems, as a means of addressing issues like the filter bubble problem and promoting transparency. In an earlier work, we developed a theoretically-sound framework named EDUSS to systematically design interactive visualizations of transparent user models for self-actualization. We aim in this paper to validate the effectiveness of using the EDUSS framework to support self-actualization. To this end, we implemented interactive visualizations of transparent user interest models designed with the help of the EDUSS framework into the transparent Recommendation and Interest Modeling Application (RIMA). Further, we conducted a qualitative user study (N=10) to investigate the effect of these visualizations in supporting users to achieve self-actualization. Our study showed qualitative evidence validating that applying the EDUSS framework to design systems for self-actualization has the potential to help users reach self-actualization goals to a certain extent.

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Cited By

Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
https://doi.org/10.3390/mti7090091
Guesmi MChatti MJoarder SAin QAlatrash RSiepmann CVahidi T(2023)Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226279740:22(7248-7269)Online publication date: 15-Oct-2023
https://doi.org/10.1080/10447318.2023.2262797

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cover image ACM Conferences

UMAP '23 Adjunct: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization

June 2023

446 pages

ISBN:9781450398916

DOI:10.1145/3563359

Copyright © 2023 ACM.

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Published: 16 June 2023

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UMAP '23: 31st ACM Conference on User Modeling, Adaptation and Personalization

June 26 - 29, 2023

Limassol, Cyprus

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Cited By

Guesmi MChatti MKadhim LJoarder SAin Q(2023)Semantic Interest Modeling and Content-Based Scientific Publication Recommendation Using Word Embeddings and Sentence EncodersMultimodal Technologies and Interaction10.3390/mti70900917:9(91)Online publication date: 15-Sep-2023
https://doi.org/10.3390/mti7090091
Guesmi MChatti MJoarder SAin QAlatrash RSiepmann CVahidi T(2023)Interactive Explanation with Varying Level of Details in an Explainable Scientific Literature Recommender SystemInternational Journal of Human–Computer Interaction10.1080/10447318.2023.226279740:22(7248-7269)Online publication date: 15-Oct-2023
https://doi.org/10.1080/10447318.2023.2262797

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