• Title/Summary/Keyword: Big Data Analytics Process

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2024 Korea Digital Business Trend Study: Listening to Voices from Academia and Industry (2024 대한민국 디지털 비즈니스 트렌드 인식조사: 학계와 산업계의 다양한 목소리를 들어보다)

  • Hajin Shin;Hyunchul Ahn;Taekyung Kim;Jung Lee
    • Information Systems Review
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    • v.26 no.1
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    • pp.315-335
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    • 2024
  • This study analyzes the digital business environment in Korea and predicts the digital business trends to be noted in 2024. The study comprehensively reviews the domestic and international ICT market outlook and provides objective and in-depth analysis by compiling opinions from various experts. In particular, through a multi-dimensional approach, it derives practical trends applicable to the local business environment, provides strategic implications considering the characteristics of digital business in Korea, and suggests directions for Korean companies to adapt to the global business environment and strengthen their competitiveness. During the research process, 20 preliminary candidate trends were initially identified by collecting and analyzing reports from major domestic and international market research institutes. We then conducted in-depth interviews with 10 experts from industry and academia to select 15 shortlisted trends from these 20 trends and 10 trends selected from the previous year. Finally, we conducted a large-scale survey of 209 experts from academia and industry, and we selected 11 domestic digital business trends to focus on in 2024. This study, which presents an outlook of digital business trends suitable for the Korean business environment based on a variety of opinions scientifically gathered from Korean digital business leaders, will contribute to understanding IT trends in Korea from a business perspective and their differences from global trends.

Conditional Generative Adversarial Network based Collaborative Filtering Recommendation System (Conditional Generative Adversarial Network(CGAN) 기반 협업 필터링 추천 시스템)

  • Kang, Soyi;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.27 no.3
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    • pp.157-173
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    • 2021
  • With the development of information technology, the amount of available information increases daily. However, having access to so much information makes it difficult for users to easily find the information they seek. Users want a visualized system that reduces information retrieval and learning time, saving them from personally reading and judging all available information. As a result, recommendation systems are an increasingly important technologies that are essential to the business. Collaborative filtering is used in various fields with excellent performance because recommendations are made based on similar user interests and preferences. However, limitations do exist. Sparsity occurs when user-item preference information is insufficient, and is the main limitation of collaborative filtering. The evaluation value of the user item matrix may be distorted by the data depending on the popularity of the product, or there may be new users who have not yet evaluated the value. The lack of historical data to identify consumer preferences is referred to as data sparsity, and various methods have been studied to address these problems. However, most attempts to solve the sparsity problem are not optimal because they can only be applied when additional data such as users' personal information, social networks, or characteristics of items are included. Another problem is that real-world score data are mostly biased to high scores, resulting in severe imbalances. One cause of this imbalance distribution is the purchasing bias, in which only users with high product ratings purchase products, so those with low ratings are less likely to purchase products and thus do not leave negative product reviews. Due to these characteristics, unlike most users' actual preferences, reviews by users who purchase products are more likely to be positive. Therefore, the actual rating data is over-learned in many classes with high incidence due to its biased characteristics, distorting the market. Applying collaborative filtering to these imbalanced data leads to poor recommendation performance due to excessive learning of biased classes. Traditional oversampling techniques to address this problem are likely to cause overfitting because they repeat the same data, which acts as noise in learning, reducing recommendation performance. In addition, pre-processing methods for most existing data imbalance problems are designed and used for binary classes. Binary class imbalance techniques are difficult to apply to multi-class problems because they cannot model multi-class problems, such as objects at cross-class boundaries or objects overlapping multiple classes. To solve this problem, research has been conducted to convert and apply multi-class problems to binary class problems. However, simplification of multi-class problems can cause potential classification errors when combined with the results of classifiers learned from other sub-problems, resulting in loss of important information about relationships beyond the selected items. Therefore, it is necessary to develop more effective methods to address multi-class imbalance problems. We propose a collaborative filtering model using CGAN to generate realistic virtual data to populate the empty user-item matrix. Conditional vector y identify distributions for minority classes and generate data reflecting their characteristics. Collaborative filtering then maximizes the performance of the recommendation system via hyperparameter tuning. This process should improve the accuracy of the model by addressing the sparsity problem of collaborative filtering implementations while mitigating data imbalances arising from real data. Our model has superior recommendation performance over existing oversampling techniques and existing real-world data with data sparsity. SMOTE, Borderline SMOTE, SVM-SMOTE, ADASYN, and GAN were used as comparative models and we demonstrate the highest prediction accuracy on the RMSE and MAE evaluation scales. Through this study, oversampling based on deep learning will be able to further refine the performance of recommendation systems using actual data and be used to build business recommendation systems.