Towards Data-Driven Decision-Making in the Korean Film Industry: An XAI Model for Box Office Analysis Using Dimension Reduction, Clustering, and Classification

被引:4
作者
Leem, Subeen [1 ]
Oh, Jisong [2 ]
So, Dayeong [3 ]
Moon, Jihoon [1 ,2 ,3 ]
机构
[1] Soonchunhyang Univ, Dept Med Sci, Asan 31538, South Korea
[2] Soonchunhyang Univ, Dept AI & Big Data, Asan 31538, South Korea
[3] Soonchunhyang Univ, Dept ICT Convergence, Asan 31538, South Korea
关键词
box office; classification; clustering; deep autoencoder; explainable artificial intelligence; machine learning; uniform manifold approximation and projection; REVENUE; MOVIES;
D O I
10.3390/e25040571
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
The Korean film market has been rapidly growing, and the importance of explainable artificial intelligence (XAI) in the film industry is also increasing. In this highly competitive market, where producing a movie incurs substantial costs, it is crucial for film industry professionals to make informed decisions. To assist these professionals, we propose DRECE (short for Dimension REduction, Clustering, and classification for Explainable artificial intelligence), an XAI-powered box office classification and trend analysis model that provides valuable insights and data-driven decision-making opportunities for the Korean film industry. The DRECE framework starts with transforming multi-dimensional data into two dimensions through dimensionality reduction techniques, grouping similar data points through K-means clustering, and classifying movie clusters through machine-learning models. The XAI techniques used in the model make the decision-making process transparent, providing valuable insights for film industry professionals to improve the box office performance and maximize profits. With DRECE, the Korean film market can be understood in new and exciting ways, and decision-makers can make informed decisions to achieve success.
引用
收藏
页数:29
相关论文
共 75 条
[1]   Visual Analytics for Dimension Reduction and Cluster Analysis of High Dimensional Electronic Health Records [J].
Abdullah, Sheikh S. ;
Rostamzadeh, Neda ;
Sedig, Kamran ;
Garg, Amit X. ;
McArthur, Eric .
INFORMATICS-BASEL, 2020, 7 (02)
[2]   Deep Clustering Bearing Fault Diagnosis Method Based on Local Manifold Learning of an Autoencoded Embedding [J].
An, Jing ;
Ai, Ping ;
Liu, Cong ;
Xu, Sen ;
Liu, Dakun .
IEEE ACCESS, 2021, 9 :30154-30168
[3]   A Deep Reinforcement Learning-Based Decision Support System for Automated Stock Market Trading [J].
Ansari, Yasmeen ;
Yasmin, Sadaf ;
Naz, Sheneela ;
Zaffar, Hira ;
Ali, Zeeshan ;
Moon, Jihoon ;
Rho, Seungmin .
IEEE ACCESS, 2022, 10 :127469-127501
[4]   Electronic word-of-mouth, box office revenue and social media [J].
Baek, Hyunmi ;
Oh, Sehwan ;
Yang, Hee-Dong ;
Ahn, JoongHo .
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2017, 22 :13-23
[5]  
Bholowalia P., 2014, Int. J. Comput. Appl, V105, P17, DOI DOI 10.5120/18405-9674
[6]   Box office sales and social media: A cross-platform comparison of predictive ability and mechanisms [J].
Bogaert, Matthias ;
Ballings, Michel ;
Van den Poel, Dirk ;
Oztekin, Asil .
DECISION SUPPORT SYSTEMS, 2021, 147
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]  
catboost, GRID SEARCH CATBOOST
[9]   Wild adaptive trimming for robust estimation and cluster analysis [J].
Cerioli, Andrea ;
Farcomeni, Alessio ;
Riani, Marco .
SCANDINAVIAN JOURNAL OF STATISTICS, 2019, 46 (01) :235-256
[10]   IT-business alignment, big data analytics capability, and strategic decision-making: Moderating roles of event criticality and disruption of COVID-19 [J].
Chen, Lifan ;
Liu, Hefu ;
Zhou, Zhongyun ;
Chen, Meng ;
Chen, Yao .
DECISION SUPPORT SYSTEMS, 2022, 161