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Personalized Educational Learning with Multi-Stakeholder Optimizations

Authors:

Nastaran Ghane,

Milad SabouriAuthors Info & Claims

UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

Pages 283 - 289

https://doi.org/10.1145/3314183.3323843

Published: 06 June 2019 Publication History

Abstract

Recommender systems (RS) have been introduced to educations as an effective technology-enhanced learning technique. Traditional RS produce recommendations by considering the preferences of the end users only. Multi-stakeholder recommender systems (MSRS) claim that it is necessary to consider the utility of the items from the perspective of other stakeholders in order to balance the needs of multiple stakeholders. Take book recommendations for example, the utility of items from the view of parents, instructors and even publishers may be also important in addition to the student preferences. In this paper, we propose and exploit utility-based MSRS for personalized learning. Particularly, we attempt to address the challenge of over-/under-expectations in the utility-based MSRS. Our experimental results based on an educational data demonstrate the effectiveness of our proposed models and solutions.

References

[1]

Gediminas Adomavicius and YoungOk Kwon. 2007. New recommendation techniques for multicriteria rating systems. IEEE Intelligent Systems, Vol. 22, 3 (2007).

Digital Library

[2]

Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018. Balanced Neighborhoods for Multi-sided Fairness in Recommendation. In Conference on Fairness, Accountability and Transparency. 202--214.

[3]

Robin Burke, Yong Zheng, and Scott Riley. 2011. Experience Discovery: hybrid recommendation of student activities using social network data. In Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems. ACM, 49--52.

Digital Library

[4]

Robin D Burke, Himan Abdollahpouri, Bamshad Mobasher, and Trinadh Gupta. 2016. Towards Multi-Stakeholder Utility Evaluation of Recommender Systems. In UMAP (Extended Proceedings) .

[5]

Kalyanmoy Deb, Manikanth Mohan, and Sikhar Mishra. 2003. A fast multi-objective evolutionary algorithm for finding well-spread pareto-optimal solutions. KanGAL report, Vol. 2003002 (2003), 1--18.

[6]

Hendrik Drachsler, Katrien Verbert, Olga C Santos, and Nikos Manouselis. 2015. Panorama of recommender systems to support learning. In Recommender systems handbook . Springer, 421--451.

[7]

Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference. ACM, 214--226.

Digital Library

[8]

Qi He, Jian Pei, Daniel Kifer, Prasenjit Mitra, and Lee Giles. 2010. Context-aware citation recommendation. In Proceedings of the 19th international conference on World wide web. ACM, 421--430.

Digital Library

[9]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer, Vol. 42, 8 (2009).

Digital Library

[10]

Nikos Manouselis, Hendrik Drachsler, Riina Vuorikari, Hans Hummel, and Rob Koper. 2011. Recommender systems in technology enhanced learning. In Recommender systems handbook . Springer, 387--415.

[11]

Phong Nguyen, John Dines, and Jan Krasnodebski. 2017. A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders. arXiv preprint arXiv:1708.00651 (2017).

[12]

Maria Soledad Pera and Yiu-Kai Ng. 2013. What to read next?: making personalized book recommendations for K-12 users. In Proceedings of the 7th ACM conference on Recommender systems. ACM, 113--120.

Digital Library

[13]

Luiz Pizzato, Tomek Rej, Thomas Chung, Kalina Yacef, Irena Koprinska, and Judy Kay. 2010. Reciprocal recommenders. In 8th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems, UMAP .

[14]

Michael J Polonsky and Michael R Hyman. 2007. A multiple stakeholder perspective on responsibility in advertising. Journal of Advertising, Vol. 36, 2 (2007), 5--13.

[15]

Marco Tulio Ribeiro, Anisio Lacerda, Adriano Veloso, and Nivio Ziviani. 2012. Pareto-efficient hybridization for multi-objective recommender systems. In Proceedings of the 6th ACM conference on Recommender systems .

Digital Library

[16]

Hamed Valizadegan, Rong Jin, Ruofei Zhang, and Jianchang Mao. 2009. Learning to rank by optimizing ndcg measure. In Advances in neural information processing systems. 1883--1891.

Digital Library

[17]

Yong Zheng. 2017. Multi-Stakeholder Recommendation: Applications and Challenges. arXiv preprint arXiv:1707.08913 (2017).

[18]

Yong Zheng. 2018a. Exploring user roles in group recommendations: a learning approach. In Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. ACM, 49--52.

Digital Library

[19]

Yong Zheng. 2018b. Identifying Dominators and Followers In Group Decision Making Based on The Personality Traits. In Companion Proceedings of the 23rd International on Intelligent User Interfaces: 2nd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces .

[20]

Yong Zheng. 2018c. Personality-Aware Decision Making In Educational Learning. In Proceedings of the 23rd International Conference on Intelligent User Interfaces Companion. ACM, 58.

Digital Library

[21]

Yong Zheng. 2018 d. Utility-Based Multi-Stakeholder Recommendations By Multi-Objective Optimization. In Proceedings of the 2018 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE.

[22]

Yong Zheng. 2019 a. Multi-Stakeholder Personalized Learning with Preference Corrections. In Proceedings of the 19th IEEE International Conference on Advanced Learning Technologies (ICALT) . IEEE.

[23]

Yong Zheng. 2019 b. Utility-Based Multi-Criteria Recommender Systems. In Proceedings of the ACM Symposium on Applied Computing. ACM.

Digital Library

[24]

Yong Zheng, Tanaya Dave, Neha Mishra, and Harshit Kumar. 2018. Fairness In Reciprocal Recommendations: A Speed-Dating Study. In Adjunct Proceedings of the ACM conference on User Modelling, Adaptation and Personalization. ACM.

Digital Library

[25]

Yong Zheng and Archana Subramaniyan. 2019. Personality-Aware Collaborative Learning: Models and Explanations. In International Conference on Advanced Information Networking and Applications. Springer, 631--642.

Cited By

Galici RKäser TFenu GMarras M(2023)How Close are Predictive Models to Teachers in Detecting Learners at Risk?Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3595620(135-145)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3595620
Puspitasari NHaviluddin HHairah USeptiarini ATappang MSamsumar L(2023)Comparison of Bayes Theorem and Dempster Shafer Methods for Detection Pests of Mayas Rice Plants2023 9th International Conference on Computer and Communication Engineering (ICCCE)10.1109/ICCCE58854.2023.10246070(229-234)Online publication date: 15-Aug-2023
https://doi.org/10.1109/ICCCE58854.2023.10246070
Shrivastava RSingh Sisodia DKumar Nagwani N(2023)Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119071213:PBOnline publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119071
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Personalized Educational Learning with Multi-Stakeholder Optimizations
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

cover image ACM Conferences

UMAP'19 Adjunct: Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization

June 2019

455 pages

ISBN:9781450367110

DOI:10.1145/3314183

General Chairs:
George Angelos Papadopoulos
University of Cyprus, Cyprus
,
George Samaras
University of Cyprus, Cyprus
,
Stephan Weibelzahl
PFH Private University of Applied Sciences Göttingen, Germany
,
Program Chairs:
Dietmar Jannach
Alpen-Adria-Universität Klagenfurt, Austria
,
Olga C. Santos
aDeNu Research Group - UNED, Spain

Copyright © 2019 ACM.

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Published: 06 June 2019

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UMAP '19

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UMAP '19: 27th Conference on User Modeling, Adaptation and Personalization

June 9 - 12, 2019

Larnaca, Cyprus

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UMAP'19 Adjunct Paper Acceptance Rate 30 of 122 submissions, 25%;

Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Galici RKäser TFenu GMarras M(2023)How Close are Predictive Models to Teachers in Detecting Learners at Risk?Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3595620(135-145)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3595620
Puspitasari NHaviluddin HHairah USeptiarini ATappang MSamsumar L(2023)Comparison of Bayes Theorem and Dempster Shafer Methods for Detection Pests of Mayas Rice Plants2023 9th International Conference on Computer and Communication Engineering (ICCCE)10.1109/ICCCE58854.2023.10246070(229-234)Online publication date: 15-Aug-2023
https://doi.org/10.1109/ICCCE58854.2023.10246070
Shrivastava RSingh Sisodia DKumar Nagwani N(2023)Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learningExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.119071213:PBOnline publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1016/j.eswa.2022.119071
Zheng Y(2023)Tutorial: Educational Recommender SystemsArtificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky10.1007/978-3-031-36336-8_7(50-56)Online publication date: 30-Jun-2023
https://doi.org/10.1007/978-3-031-36336-8_7
Paparella V(2022)Pursuing Optimal Trade-Off Solutions in Multi-Objective Recommender SystemsProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3547425(727-729)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3547425
Zheng YWang D(2022)A survey of recommender systems with multi-objective optimizationNeurocomputing10.1016/j.neucom.2021.11.041474:C(141-153)Online publication date: 14-Feb-2022
https://dl.acm.org/doi/10.1016/j.neucom.2021.11.041
Adomavicius GBockstedt JCurley SZhang J(2022)Recommender systems, ground truth, and preference pollutionAI Magazine10.1002/aaai.1205543:2(177-189)Online publication date: 23-Jun-2022
https://dl.acm.org/doi/10.1002/aaai.12055
Yilma BNaudet YPanetto H(2021)Personalisation in Cyber-Physical-Social Systems: A Multi-stakeholder aware Recommendation and GuidanceProceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450613.3456847(251-255)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450613.3456847
Zheng YWang DZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)Multi-Objective RecommendationsProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3470788(4098-4099)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3470788
Zheng YToribio J(2021)The role of transparency in multi-stakeholder educational recommendationsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09291-x31:3(513-540)Online publication date: 7-Apr-2021
https://doi.org/10.1007/s11257-021-09291-x
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