short-paper

Open access

Personalisation in Cyber-Physical-Social Systems: A Multi-stakeholder aware Recommendation and Guidance

Authors:

Bereket Abera Yilma,

Yannick Naudet,

Hervé PanettoAuthors Info & Claims

UMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

Pages 251 - 255

https://doi.org/10.1145/3450613.3456847

Published: 21 June 2021 Publication History

All formats PDF

Abstract

The evolution of smart devices has led to the transformation of many physical spaces to the so-called smart environments collectively termed as Cyber-Physical-Social System (CPSS). In CPSS users co-exist with different stakeholders influencing each other while being influenced by different environmental factors. Additionally, these environments often have their own desired goals and corresponding set of rules in place expecting people to behave in certain ways. Hence, in such settings classical approaches to personalisation which solely optimise for user satisfaction are often encumbered by competing objectives and environmental constraints which are yet to be addressed jointly. In this work we set out to (i) formalise the general problem of personalisation in CPSS from a multi-stakeholder perspective taking into account the full environmental complexity, (ii) extend the general formalisation to the case of exhibition areas and propose a personalised Multi-stakeholder aware Recommendation and Guidance method on a case study of National Gallery, London.

Supplementary Material

MP4 File (UMAP21-sp11341.mp4)

The evolution of smart devices has led to the transformation of many physical spaces to the so-called smart environments collectively termed as Cyber-Physical-Social System (CPSS). In CPSS users co-exist with different stakeholders influencing each other while being influenced by different environmental factors. Additionally, these environments often have their own desired goals and corresponding set of rules in place expecting people to behave in certain ways. Hence, in such settings classical approaches to personalisation which solely optimise for user satisfaction are often encumbered by competing objectives and environmental constraints which are yet to be addressed jointly. In this work we set out to (i) formalise the general problem of personalisation in CPSS from a multi-stakeholder perspective taking into account the full environmental complexity, (ii) extend the general formalisation to the case of exhibition areas and propose a personalised Multi-stakeholder aware Recommendation and Guidance method on a case study of National Gallery, London.

Download
16.86 MB

References

[1]

Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, and Luiz Pizzato. 2020. Multistakeholder recommendation: Survey and research directions. User Modeling and User-Adapted Interaction 30, 1 (2020), 127–158.

[2]

Himan Abdollahpouri and Robin Burke. 2019. Multi-stakeholder recommendation and its connection to multi-sided fairness. arXiv preprint arXiv:1907.13158(2019).

[3]

Bereket Abera Yilma, Najib Aghenda, Marcelo Romero, Yannick Naudet, and Herve Panetto. 2020. Personalised Visual Art Recommendation by Learning Latent Semantic Representations. arXiv e-prints (2020), arXiv–2008.

[4]

Lyuba Alboul, Martin Beer, and Louis Nisiotis. 2019. Robotics and virtual reality gaming for cultural heritage preservation. (2019).

[5]

Saeed Amal, Mustafa Adam, Peter Brusilovsky, Einat Minkov, Zef Segal, and Ts vi Kuflik. 2020. Demonstrating Personalized Multifaceted Visualization of People Recommendation to Conference Participants. In Proceedings of the 25th International Conference on Intelligent User Interfaces Companion. 49–50.

Digital Library

[6]

Christos G Cassandras. 2016. Smart cities as cyber-physical social systems. Engineering 2, 2 (2016), 156–158.

[7]

Sylvain Castagnos, Florian Marchal, Alexandre Bertrand, Morgane Colle, and Djalila Mahmoudi. 2019. Inferring Art Preferences from Gaze Exploration in a Museum. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. 425–430.

Digital Library

[8]

María del Carmen Rodríguez-Hernández, Sergio Ilarri, Ramón Hermoso, and Raque Trillo-Lado. 2017. Towards trajectory-based recommendations in museums: evaluation of strategies using mixed synthetic and real data. Procedia computer science 113 (2017), 234–239.

[9]

SeoYoung Lee and Junho Choi. 2017. Enhancing user experience with conversational agent for movie recommendation: Effects of self-disclosure and reciprocity. International Journal of Human-Computer Studies 103 (2017), 95–105.

Digital Library

[10]

Ting-Peng Liang, Hung-Jen Lai, and Yi-Cheng Ku. 2006. Personalized content recommendation and user satisfaction: Theoretical synthesis and empirical findings. Journal of Management Information Systems 23, 3 (2006), 45–70.

Digital Library

[11]

Eran Litvak and Tsvi Kuflik. 2020. Enhancing cultural heritage outdoor experience with augmented-reality smart glasses. Personal and Ubiquitous Computing(2020), 1–14.

[12]

Ioanna Lykourentzou, Angeliki Antoniou, Yannick Naudet, and Steven P Dow. 2016. Personality matters: Balancing for personality types leads to better outcomes for crowd teams. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing. 260–273.

Digital Library

[13]

Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018. Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In Proceedings of the 27th acm international conference on information and knowledge management. 2243–2251.

Digital Library

[14]

Rishabh Mehrotra and Emine Yilmaz. 2015. Representative & informative query selection for learning to rank using submodular functions. In Proceedings of the 38th international ACM sigir conference on research and development in information retrieval. 545–554.

Digital Library

[15]

Lukáš Najbrt and Jana Kapounová. 2014. Categorization of museum visitors as part of system for personalized museum tour. International Journal of Information and Communication Technologies in Education 3, 1 (2014), 17–27.

[16]

Yannick Naudet, Bereket Abera Yilma, and Hervé Panetto. 2018. Personalisation in cyber physical and social systems: the case of recommendations in cultural heritage spaces. In 2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). IEEE, 75–79.

[17]

Louis Nisiotis, Lyuba Alboul, and Martin Beer. 2020. A Prototype that Fuses Virtual Reality, Robots, and Social Networks to Create a New Cyber–Physical–Social Eco-Society System for Cultural Heritage. Sustainability 12, 2 (2020), 645.

[18]

Pierre-Edouard Osche, Sylvain Castagnos, Amedeo Napoli, and Yannick Naudet. 2016. Walk the line: Toward an efficient user model for recommendations in museums. In 2016 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). IEEE, 83–88.

[19]

Pearl Pu, Li Chen, and Rong Hu. 2011. A user-centric evaluation framework for recommender systems. In Proceedings of the fifth ACM conference on Recommender systems. 157–164.

Digital Library

[20]

Eirini Eleni Tsiropoulou, George Kousis, Athina Thanou, Ioanna Lykourentzou, and Symeon Papavassiliou. 2018. Quality of experience in cyber-physical social systems based on reinforcement learning and game theory. Future Internet 10, 11 (2018), 108.

[21]

Eirini Eleni Tsiropoulou, Athina Thanou, and Symeon Papavassiliou. 2016. Modelling museum visitors’ Quality of Experience. In 2016 11th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). IEEE, 77–82.

[22]

Eirini Eleni Tsiropoulou, Athina Thanou, and Symeon Papavassiliou. 2017. Quality of Experience-based museum touring: A human in the loop approach. Social Network Analysis and Mining 7, 1 (2017), 33.

[23]

Willème Verdeaux, Clément Moreau, Nicolas Labroche, and Patrick Marcel. 2020. Causality based explanations in multi-stakeholder recommendations. In EDBT/ICDT Workshops.

[24]

Bereket Abera Yilma, Yannick Naudet, and Hervé Panetto. 2018. Introduction to personalisation in cyber-physical-social systems. In OTM Confederated International Conferences” On the Move to Meaningful Internet Systems”. Springer, 25–35.

[25]

Bereket Abera Yilma, Yannick Naudet, and Hervé Panetto. 2020. A new paradigm and meta-model for cyber-physical-social systems. In 21st IFAC World Congress, IFAC 2020. Elsevier.

[26]

Bereket Abera Yilma, Hervé Panetto, and Yannick Naudet. 2019. A Meta-Model of Cyber-Physical-Social System: The CPSS paradigm to support Human-Machine collaboration in Industry 4.0. In Working Conference on Virtual Enterprises. Springer, 11–20.

[27]

Jun Jason Zhang, Fei-Yue Wang, Xiao Wang, Gang Xiong, Fenghua Zhu, Yisheng Lv, Jiachen Hou, Shuangshuang Han, Yong Yuan, Qingchun Lu, 2018. Cyber-physical-social systems: The state of the art and perspectives. IEEE Transactions on Computational Social Systems 5, 3 (2018), 829–840.

[28]

Yong Zheng. 2019. Multi-stakeholder personalized learning with preference corrections. In 2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), Vol. 2161. IEEE, 66–70.

[29]

Yong Zheng. 2019. Multi-stakeholder recommendations: case studies, methods and challenges. In Proceedings of the 13th ACM Conference on Recommender Systems. 578–579.

Digital Library

[30]

Yong Zheng, Nastaran Ghane, and Milad Sabouri. 2019. Personalized educational learning with multi-stakeholder optimizations. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization. 283–289.

Digital Library

Cited By

Yilma BLeiva L(2023)Together Yet Apart: Multimodal Representation Learning for Personalised Visual Art RecommendationProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592964(204-214)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592964

Recommendations

A fairness-aware multi-stakeholder recommender system
Abstract
Traditional recommender systems mainly focus on the accuracy of recommendation, which lead to recommender systems reinforcing popular items and ignoring lesser-known items. There is increasing evidence that providing good recommendations of ...
Dynamic context-aware personalisation in a pervasive environment

In the development of ubiquitous and pervasive systems, it is understood that mechanisms are required to take adequate account of user preferences. This paper presents several key challenges for personalisation in pervasive environments and introduces ...
Incorporating reliable virtual ratings into social recommendation systems

Social recommendation systems use social relations (such as trust, friendship, etc.) among users to find preferences and provide relevant suggestions to users. Historical ratings of items provided by the users are also used to predict unseen items in ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization

June 2021

325 pages

ISBN:9781450383660

DOI:10.1145/3450613

Editors:
Judith Masthoff
Utrecht University, The Netherlands and University of Aberdeen, UK
,
Eelco Herder
Radboud University, The Netherlands
,
Nava Tintarev
University of Maastricht, The Netherlands
,
Marko Tkalčič
University of Primorska, Slovenia

Copyright © 2021 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 ACM 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: 21 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Funding Sources

LUXEMBOURG INSTITUTE OF SCIENCE AND TECHNOLOGY

Conference

UMAP '21

Sponsor:

UMAP '21: 29th ACM Conference on User Modeling, Adaptation and Personalization

June 21 - 25, 2021

Utrecht, Netherlands

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

1
Total Citations
View Citations
577
Total Downloads

Downloads (Last 12 months)111
Downloads (Last 6 weeks)11

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yilma BLeiva L(2023)Together Yet Apart: Multimodal Representation Learning for Personalised Visual Art RecommendationProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3592964(204-214)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3592964

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

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

Media

Figures

Other

Tables

View Table of Contents