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Special Issue on Data-Driven Personality Modeling for Intelligent Human-Computer Interaction

Special Issue on Data-Driven Personality Modeling for Intelligent Human-Computer Interaction Special Issue on Data-Driven Personality Modeling for Intelligent Human-Computer Interaction SHIMEI PAN, University of Maryland, Baltimore County OLIVER BRDICZKA, Adobe Inc. ANDREA KLEINSMITH, University of Maryland, Baltimore County YANGQIU SONG, Hong Kong University of Science and Technology CCS Concepts: • Human-centered computing → User models;• Computing methodologies → Ma- chine learning; Additional Key Words and Phrases: Personality modeling, machine learning, user modeling, personalization ACM Reference format: Shimei Pan, Oliver Brdiczka, Andrea Kleinsmith, and Yangqiu Song. 2020. Special Issue on Data-Driven Per- sonality Modeling for Intelligent Human-Computer Interaction. ACM Trans. Interact. Intell. Syst. 10, 3, Article 17 (November 2020), 3 pages. https://doi.org/10.1145/3402522 Recent advances in Artificial Intelligence (AI) and data analytics have enabled new forms of human-computer interaction characterized by greater adaptability and better human-machine symbiosis. To facilitate the development of next generation intelligent systems that can truly un- derstand and interact with humans, it is important that they can understand and adapt to individ- ual differences and personality traits. Here we define the word “personality” broadly to refer to the patterns of human thoughts, feelings, social adjustments, and behaviors consistently exhibited over time that strongly influence one’s expectations, self-perceptions, values, and attitudes. This special issue explores research frontiers in http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Interactive Intelligent Systems (TiiS) Association for Computing Machinery

Special Issue on Data-Driven Personality Modeling for Intelligent Human-Computer Interaction

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2020 ACM
ISSN
2160-6455
eISSN
2160-6463
DOI
10.1145/3402522
Publisher site
See Article on Publisher Site

Abstract

Special Issue on Data-Driven Personality Modeling for Intelligent Human-Computer Interaction SHIMEI PAN, University of Maryland, Baltimore County OLIVER BRDICZKA, Adobe Inc. ANDREA KLEINSMITH, University of Maryland, Baltimore County YANGQIU SONG, Hong Kong University of Science and Technology CCS Concepts: • Human-centered computing → User models;• Computing methodologies → Ma- chine learning; Additional Key Words and Phrases: Personality modeling, machine learning, user modeling, personalization ACM Reference format: Shimei Pan, Oliver Brdiczka, Andrea Kleinsmith, and Yangqiu Song. 2020. Special Issue on Data-Driven Per- sonality Modeling for Intelligent Human-Computer Interaction. ACM Trans. Interact. Intell. Syst. 10, 3, Article 17 (November 2020), 3 pages. https://doi.org/10.1145/3402522 Recent advances in Artificial Intelligence (AI) and data analytics have enabled new forms of human-computer interaction characterized by greater adaptability and better human-machine symbiosis. To facilitate the development of next generation intelligent systems that can truly un- derstand and interact with humans, it is important that they can understand and adapt to individ- ual differences and personality traits. Here we define the word “personality” broadly to refer to the patterns of human thoughts, feelings, social adjustments, and behaviors consistently exhibited over time that strongly influence one’s expectations, self-perceptions, values, and attitudes. This special issue explores research frontiers in

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)Association for Computing Machinery

Published: Nov 18, 2020

Keywords: Personality modeling

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