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.

References

[1]

Krisztian Balog, Filip Radlinski, and Shushan Arakelyan. 2019. Transparent, scrutable and explainable user models for personalized recommendation. In Proceedings of the 42nd international acm sigir conference on research and development in information retrieval. 265–274.

Digital Library

[2]

David Benyon and Dianne Murray. 1993. Applying user modeling to human-computer interaction design. Artificial Intelligence Review 7 (1993), 199–225.

[3]

Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology. Qualitative research in psychology 3, 2 (2006), 77–101.

[4]

Mohamed Amine Chatti, Mouadh Guesmi, Laura Vorgerd, Thao Ngo, Shoeb Joarder, Qurat Ul Ain, and Arham Muslim. 2022. Is more always better? the effects of personal characteristics and level of detail on the perception of explanations in a recommender system. In Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 254–264.

Digital Library

[5]

Gerhard Fischer. 2001. User modeling in human–computer interaction. User modeling and user-adapted interaction 11 (2001), 65–86.

[6]

Mouzhi Ge, Carla Delgado-Battenfeld, and Dietmar Jannach. 2010. Beyond accuracy: evaluating recommender systems by coverage and serendipity. In Proceedings of the fourth ACM conference on Recommender systems. 257–260.

Digital Library

[7]

Kurt Goldstein. 1940. Human nature in the light of psychopathology. In Human Nature in the Light of Psychopathology. Harvard University Press.

[8]

D Graus, M Sappelli, and D Manh Chu. 2018. " let me tell you who you are"-Explaining recommender systems by opening black box user profiles. In The 2nd fatrec workshop on responsible recommendation. [Sl: sn].

[9]

M Guesmi, M Chatti, Y Sun, S Zumor, F Ji, A Muslim, L Vorgerd, and SA Joarder. 2021. Open, scrutable and explainable interest models for transparent recommendation. In Proceedings of the IUI Workshops.

[10]

M Guesmi, MA Chatti, L Vorgerd, and others. 2021. Input or Output: Effects of Explanation Focus on the Perception of Explainable Recommendation with Varying Level of Details. In Proceedings of the CEUR Workshop Proceedings, Vol. 2948. 55–72.

[11]

Mouadh Guesmi, Mohamed Amine Chatti, Jaleh Ghorbani-Bavani, Shoeb Joarder, Qurat Ul Ain, and Rawaa Alatrash. 2022. What if Interactive Explanation in a Scientific Literature Recommender System. (2022).

[12]

Mouadh Guesmi, Mohamed Amine Chatti, Alptug Tayyar, Qurat Ul Ain, and Shoeb Joarder. 2022. Interactive Visualizations of Transparent User Models for Self-Actualization: A Human-Centered Design Approach. Multimodal Technologies and Interaction 6, 6 (2022), 42.

[13]

Mouadh Guesmi, Mohamed Amine Chatti, Laura Vorgerd, Shoeb Joarder, Shadi Zumor, Yiqi Sun, Fangzheng Ji, and Arham Muslim. 2021. On-demand personalized explanation for transparent recommendation. In Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization. 246–252.

Digital Library

[14]

Mouadh Guesmi, Mohamed Amine Chatti, Laura Vorgerd, Thao Ngo, Shoeb Joarder, Qurat Ul Ain, and Arham Muslim. 2022. Explaining user models with different levels of detail for transparent recommendation: A user study. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 175–183.

Digital Library

[15]

Lijie Guo. 2018. Beyond the top-N: algorithms that generate recommendations for self-actualization. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, Vancouver British Columbia Canada, 573–577. https://doi.org/10.1145/3240323.3240330

Digital Library

[16]

Jaron Harambam, Dimitrios Bountouridis, Mykola Makhortykh, and Joris van Hoboken. 2019. Designing for the better by taking users into account: a qualitative evaluation of user control mechanisms in (news) recommender systems. In Proceedings of the 13th ACM Conference on Recommender Systems. ACM, Copenhagen Denmark, 69–77. https://doi.org/10.1145/3298689.3347014

Digital Library

[17]

Kristina Höök. 2000. Steps to take before intelligent user interfaces become real. Interacting with computers 12, 4 (2000), 409–426.

[18]

Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer. 2020. SqueezeBERT: What can computer vision teach NLP about efficient neural networks?arxiv:2006.11316 [cs.CL]

[19]

Alvin Jones and Rick Crandall. 1986. Validation of a short index of self-actualization. Personality and Social Psychology Bulletin 12, 1 (1986), 63–73.

[20]

Todd B Kashdan and Michael F Steger. 2007. Curiosity and pathways to well-being and meaning in life: Traits, states, and everyday behaviors. Motivation and Emotion 31 (2007), 159–173.

[21]

Tim Kasser and Richard M Ryan. 1996. Further examining the American dream: Differential correlates of intrinsic and extrinsic goals. Personality and social psychology bulletin 22, 3 (1996), 280–287.

[22]

Judy Kay. 2008. Lifelong Learner Modeling for Lifelong Personalized Pervasive Learning. IEEE Transactions on Learning Technologies 1, 4 (2008), 215–228. https://doi.org/10.1109/TLT.2009.9

Digital Library

[23]

Judy Kay and Bob Kummerfeld. 2013. Creating personalized systems that people can scrutinize and control: Drivers, principles and experience. ACM Transactions on Interactive Intelligent Systems (TiiS) 2, 4 (2013), 1–42.

[24]

Bart P Knijnenburg, Saadhika Sivakumar, and Daricia Wilkinson. 2016. Recommender systems for self-actualization. In Proceedings of the 10th acm conference on recommender systems. 11–14.

Digital Library

[25]

Johannes Kunkel, Benedikt Loepp, and Jürgen Ziegler. 2017. A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering. In Proceedings of the 22nd International Conference on Intelligent User Interfaces. ACM, Limassol Cyprus, 3–15. https://doi.org/10.1145/3025171.3025189

Digital Library

[26]

Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, and Christian Bizer. 2014. DBpedia - A Large-scale, Multilingual Knowledge Base Extracted from Wikipedia. Semantic Web Journal 6 (01 2014). https://doi.org/10.3233/SW-140134

[27]

Yu Liang and Martijn C Willemsen. 2019. Personalized recommendations for music genre exploration. In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. 276–284.

Digital Library

[28]

Abraham H Maslow. 1954. Motivation and personality Harper and Row. New York, NY (1954).

[29]

Abraham H Maslow. 1962. Toward a psychology of being. Simon and Schuster.

[30]

Pablo Mendes, Max Jakob, Andrés García-Silva, and Christian Bizer. 2011. DBpedia spotlight: Shedding light on the web of documents. ACM International Conference Proceeding Series, 1–8. https://doi.org/10.1145/2063518.2063519

Digital Library

[31]

Cataldo Musto, Marco Polignano, Giovanni Semeraro, Marco de Gemmis, and Pasquale Lops. 2020. Myrror: a platform for holistic user modeling: Merging data from social networks, smartphones and wearable devices. User Modeling and User-Adapted Interaction 30 (2020), 477–511.

Digital Library

[32]

Sayooran Nagulendra and Julita Vassileva. 2014. Understanding and controlling the filter bubble through interactive visualization: a user study. In Proceedings of the 25th ACM conference on Hypertext and social media. 107–115.

Digital Library

[33]

Sayooran Nagulendra and Julita Vassileva. 2016. Providing awareness, explanation and control of personalized filtering in a social networking site. Information Systems Frontiers 18 (2016), 145–158.

Digital Library

[34]

Behnam Rahdari, Peter Brusilovsky, and Dmitriy Babichenko. 2020. Personalizing information exploration with an open user model. In Proceedings of the 31st ACM Conference on Hypertext and Social Media. 167–176.

Digital Library

[35]

Christine Robitschek. 1998. Personal growth initiative: The construct and its measure. Measurement and evaluation in counseling and development 30, 4 (1998), 183–198.

[36]

Carl R Rogers. 1951. Client-centered. Therapy (1951), 515–520.

[37]

Emily Sullivan, Dimitrios Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, Mykola Makhortykh, Domokos Kelen, Daricia Wilkinson, David Graus, and Nava Tintarev. 2019. Reading news with a purpose: Explaining user profiles for self-actualization. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. 241–245.

Digital Library

[38]

Emily Sullivan, Dimitrios Bountouridis, Jaron Harambam, Shabnam Najafian, Felicia Loecherbach, Mykola Makhortykh, Domokos Kelen, Daricia Wilkinson, David Graus, and Nava Tintarev. 2019. Reading News with a Purpose: Explaining User Profiles for Self-Actualization. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. ACM, Larnaca Cyprus, 241–245. https://doi.org/10.1145/3314183.3323456

Digital Library

[39]

Yi Sun, Hangping Qiu, Yu Zheng, Zhongwei Wang, and Chaoran Zhang. 2020. SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model. IEEE Access 8 (2020), 10896–10906. https://doi.org/10.1109/ACCESS.2020.2965087

[40]

Nava Tintarev, Byungkyu Kang, Tobias Höllerer, and John O’Donovan. 2015. Inspection Mechanisms for Community-based Content Discovery in Microblogs. In IntRS@ RecSys. 21–28.

[41]

Nava Tintarev and Judith Masthoff. 2011. Designing and evaluating explanations for recommender systems. In Recommender systems handbook. Springer, 479–510.

[42]

Nava Tintarev, Shahin Rostami, and Barry Smyth. 2018. Knowing the unknown: visualising consumption blind-spots in recommender systems. In Proceedings of the 33rd annual ACM symposium on applied computing. 1396–1399.

Digital Library

[43]

Katrien Verbert, Denis Parra, Peter Brusilovsky, and Erik Duval. 2013. Visualizing recommendations to support exploration, transparency and controllability. In Proceedings of the 2013 international conference on Intelligent user interfaces. 351–362.

Digital Library

[44]

Rainer Wasinger, James Wallbank, Luiz Pizzato, Judy Kay, Bob Kummerfeld, Matthias Böhmer, and Antonio Krüger. 2013. Scrutable user models and personalised item recommendation in mobile lifestyle applications. In International Conference on User Modeling, Adaptation, and Personalization. Springer, 77–88.

[45]

Daricia Wilkinson. 2018. Testing a recommender system for self-actualization. In Proceedings of the 12th ACM Conference on Recommender Systems. ACM, Vancouver British Columbia Canada, 543–547. https://doi.org/10.1145/3240323.3240324

Digital Library

[46]

Daricia Wilkinson, Saadhika Sivakumar, Pratitee Sinha, and Bart P Knijnenburg. 2018. Testing a Recommender System for Self-Actualization. In Proceedings of the 12th ACM Conference on Recommender Systems (Vancouver, British Columbia, Canada) (RecSys ’18). Association for Computing Machinery, New York, NY, USA, 543–547. https://doi.org/10.1145/3240323.3240324

Digital Library

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 Interaction10.1080/10447318.2023.2262797(1-22)Online publication date: 15-Oct-2023
https://doi.org/10.1080/10447318.2023.2262797

Recommendations

Beyond the top-N: algorithms that generate recommendations for self-actualization
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Recommender systems traditionally provide users with recommendations that match their preferences, which creates a personalized user experience and increases users' satisfaction. However, recommendations from traditional systems may sometimes be ...
Cross-representation mediation of user models

Personalization is considered a powerful methodology for improving the effectiveness of information search and decision making. It has led to the dissemination of systems capable of suggesting relevant and personalized information (or items) to the users,...
Testing a recommender system for self-actualization
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems

Traditionally, recommender systems are built with the goal of aiding users' decision-making process by extrapolating what they like and what they have done to predict what they want next. However, in attempting to personalize the suggestions to users' ...

Comments

Information & Contributors

Information

Published In

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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

UMAP '23

Sponsor:

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

June 26 - 29, 2023

Limassol, Cyprus

Acceptance Rates

Overall Acceptance Rate 162 of 633 submissions, 26%

Upcoming Conference

UMAP '25

Sponsor:
sigchi
sigchi

33rd ACM Conference on User Modeling, Adaptation and Personalization

June 16 - 19, 2025

New York City , NY , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
76
Total Downloads

Downloads (Last 12 months)36
Downloads (Last 6 weeks)4

Reflects downloads up to 17 Oct 2024

Other Metrics

View Author Metrics

Citations

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 Interaction10.1080/10447318.2023.2262797(1-22)Online publication date: 15-Oct-2023
https://doi.org/10.1080/10447318.2023.2262797

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents