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      Understanding asymmetric synergistic effect between movie actors

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      PLOS ONE
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          Abstract

          Synergistic effects between movie actors have been regarded as an important indicator when they are casted in a new movie. People simply assume that synergistic effect is symmetric. The aim of this study is to understand the asymmetric synergy between actors. We propose an asymmetric synergy measurement method for actor’s star-power-based costarring movies to understand the synergistic effect. When measuring the synergy, we designed it so that it would be possible to measure the synergy that varies with the time of the costarring movie’s release and the synergy of new actors. Measured synergies were analyzed on an actor’s synergy and asymmetric synergy between actors to examine the characteristics of highly synergistic actors and the asymmetric synergy between actors. Moreover, we confirmed that measuring synergies asymmetrically demonstrated better prediction performance in various evaluation metrics (accuracy, precision, recall, and F1-score) than measuring synergies symmetrically through the synergy prediction experiment using synergy and asymmetric synergy.

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          Most cited references26

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                Author and article information

                Contributors
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2023
                20 April 2023
                : 18
                : 4
                : e0284613
                Affiliations
                [001] Department of Computer Engineering, Chung-Ang University, Seoul, Korea
                The Catholic University of Korea, KOREA, REPUBLIC OF
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0003-0050-7445
                Article
                PONE-D-23-03102
                10.1371/journal.pone.0284613
                10118101
                37079545
                6d6737a1-2387-4f19-ac9f-72b714b716a6
                © 2023 Ko, Jung

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 February 2023
                : 4 April 2023
                Page count
                Figures: 5, Tables: 2, Pages: 12
                Funding
                Funded by: National Research Foundation of Korea
                Award ID: NRF-2020R1A2B5B01002207
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100013129, Ministry of SMEs and Startups;
                Award ID: S3228977
                Award Recipient :
                Initials of the authors who received award: Jung, J.J. 1) Grant numbers awarded to the author: NRF-2020R1A2B5B01002207 The full name of each funder: National Research Foundation of Korea 2) Grant numbers awarded to the author: S3228977 The full name of each funder: Ministry of SMEs and Startups The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Engineering and Technology
                Measurement
                Computer and Information Sciences
                Data Management
                Data Mining
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Memory
                Memory Recall
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Memory
                Memory Recall
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Cluster Analysis
                K Means Clustering
                Physical Sciences
                Mathematics
                Optimization
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Unsupervised Machine Learning
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Custom metadata
                The dataset and source code in this study are available at the following GitHub database ( https://github.com/kecau/Interaction-Synergistic-Effect).

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                Uncategorized

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