• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.1
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• Journal of KIISE:Software and Applications
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• v.35 no.8
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• pp.512-521
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• 2008

Web-based information services should be evaluated for accessibility and usability with various types of Internet Web-browsing devices, interacting with web information servers. A reliable ubiquitous Web information server, accessible and usable with a variety of Web-browsing devices (e.g. a full-browsing mobile phone), should be a unified center for personalized ubiquitous Web information services as well as for business models based on personalized advertisements. We studied an estimation of the accessibility and usability in Web interaction for personalized ubiquitous Web information services, as metrics for real-time estimation. We show empirical results based on implementation and experiments in Korea, Japan and China, using a test-bed Web site ('ktrip.net') and single-character Korean domain names (e.g. 김.net, 이.net, 박.net, 최.net, ㄱ.net, ㄴ.net ... ㅎ.net, ㅏ.net, ... ㅔ.net, ㄱ.com, ㄴ.com ... ㅎ.com).

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.37-45
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• 2023

Adaptive has gained significant attention in Education Technology (EdTech), with personalized learning experiences becoming increasingly important. Next-generation chatbots, including models like ChatGPT, are emerging in the field of education. These advanced tools show great potential for delivering personalized and adaptive learning experiences. This paper reviews previous research on adaptive learning and the role of chatbots in education. Based on this, the paper explores current and future chatbot technologies to propose a framework for using ChatGPT or similar chatbots in adaptive learning. The framework includes personalized design, targeted resources and feedback, multi-turn dialogue models, reinforcement learning, and fine-tuning. The proposed framework also considers learning attributes such as age, gender, cognitive ability, prior knowledge, pacing, level of questions, interaction strategies, and learner control. However, the proposed framework has yet to be evaluated for its usability or effectiveness in practice, and the applicability of the framework may vary depending on the specific field of study. Through proposing this framework, we hope to encourage learners to more actively leverage current technologies, and likewise, inspire educators to integrate these technologies more proactively into their curricula. Future research should evaluate the proposed framework through actual implementation and explore how it can be adapted to different domains of study to provide a more comprehensive understanding of its potential applications in adaptive learning.

• Journal of Microbiology and Biotechnology
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• v.32 no.12
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• pp.1497-1505
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• 2022

Recently, the concept of personalized nutrition has been developed, which states that food components do not always lead to the same metabolic responses, but vary from person to person. Although this concept has been studied based on individual genetic backgrounds, researchers have recently explored its potential role in the gut microbiome. The gut microbiota physiologically communicates with humans by forming a bidirectional relationship with the micronutrients, macronutrients, and phytochemicals consumed by the host. Furthermore, the gut microbiota can vary from person to person and can be easily shifted by diet. Therefore, several recent studies have reported the application of personalized nutrition to intestinal microflora. This review provides an overview of the interaction of diet with the gut microbiome and the latest evidence in understanding the inter-individual differences in dietary responsiveness according to individual baseline gut microbiota and microbiome-associated dietary intervention in diseases. The diversity of the gut microbiota and the presence of specific microorganisms can be attributed to physiological differences following dietary intervention. The difference in individual responsiveness based on the gut microbiota has the potential to become an important research approach for personalized nutrition and health management, although further well-designed large-scale studies are warranted.

• International Journal of Advanced Culture Technology
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• v.10 no.2
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• pp.269-280
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• 2022

It has been common since ancient times to use fashion accessories to make a positive impression on others and develop personal brands. For this manuscript, I've designed a range of personalized electronic sounds and perfume accessories that use personalized scents and sounds to create memorable experiences. These have built-in auditory and olfactory actuators that can generate unique sound and fragrance signatures from the wearer and the other party. Based on the design explorations, I also discussed the issues of design and implementation of personalized electronic sound and fragrance accessories, as well as issues and additional business opportunities for the use of these personalized electronic sound and odor accessories in everyday life.

• Journal of Multimedia Information System
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• v.4 no.3
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• pp.123-144
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• 2017

Personalized media includes user-targeted and user-generated content (UGC) exchanged through social media and interactive applications. The increased consumption of UGC presents challenges and opportunities to multimedia information systems. We work towards modeling a deep structure for content networks. To gain insights, a hybrid practice with Media Framework (MF) is presented for network creation of personalized media, which leverages the authoring methodology with user-generated semantics. The system's vertical integration allows users to audition their personalized media networks in the context of a global system network. A navigation scheme with dynamic GUI shifts the interaction paradigm for content query and sharing. MF adopts a multimodal architecture anticipating emerging use cases and genres. To model diversification of platforms, information processing is robust across multiple technology configurations. Physical and virtual networks are integrated with distributed services and transactions, IoT, and semantic networks representing media content. MF applies spatiotemporal and semantic signal processing to differentiate action responsiveness and information responsiveness. The extension of multimedia information processing into authoring enables generating interactive and impermanent media on computationally enabled devices. The outcome of this integrated approach with presented methodologies demonstrates a paradigmatic shift of the concept of UGC as personalized media network, which is dynamical and evolvable.

• Journal of The Korean Association For Science Education
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• v.16 no.4
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• pp.401-409
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• 1996

In this study, the perceptions of science classroom climate were investigated for 360 elementary and middle school students in mixed classes. The instrument used was an adapted version of the Student Perception Questionnaire (SPQ), which consists of five elements-Participatory Climate, Personalized Interaction, Student Assertiveness, Positive Teacher, and Negative Teacher. The results indicated that the gender differences in the perceptions of the Participatory Climate and the Positive Teacher were not significant for middle school students. However, the differences were found to be significant in the perceptions of the Negative Teacher, the Personalized Interaction and the Student Assertiveness, which measure the climate for the individual student. On the other hand, elementary male and female students did not significantly differ in the perceptions of science classroom climate except one item on the Participatory Climate. Educational implications are discussed.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.43-56
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• 2023

With the development of the e-commerce market, various types of products continue to be released. However, customers face an information overload problem in purchasing decision-making. Therefore, personalized recommendations have become an essential service in providing personalized products to customers. Recently, many studies on GCN-based recommender systems have been actively conducted. Such a methodology can address the limitation in disabling to effectively reflect the interaction between customer and product in the embedding process. However, previous studies mainly use implicit feedback data to conduct experiments. Although implicit feedback data improves the data scarcity problem, it cannot represent customers' preferences for specific products. Therefore, this study proposed a novel model combining explicit and implicit feedback to address such a limitation. This study treats the average ratings of customers and products as the features of customers and products and converts them into a high-dimensional feature vector. Then, this study combines ID embedding vectors and feature vectors in the embedding layer to learn the customer-product interaction effectively. To evaluate recommendation performance, this study used the MovieLens dataset to conduct various experiments. Experimental results showed the proposed model outperforms the state-of-the-art. Therefore, the proposed model in this study can provide an enhanced recommendation service for customers to address the information overload problem.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1160-1163
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• 2021

DOOH(Degital Out of Home) advertisement market is developing steadily, and the case of use is also increasing, In advertisement market, personalized services is actively being provided with technological development. On the other hand, personalized services are difficult to be provided in DOOH and are p rovided by only personal information, not feelings. This study aims to construct personalized DOOH se rvices by using AI facial expression recognition and suggesting a solution optimized for interaction bet ween user and services by providing healing and advertisement.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.360-374
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• 2024

A personalized course recommendation algorithm based on deep learning in an online education cloud platform is proposed to address the challenges associated with effective information extraction and insufficient feature extraction. First, the user potential preferences are obtained through the course summary, course review information, user course history, and other data. Second, by embedding, the word vector is turned into a low-dimensional and dense real-valued vector, which is then fed into the compressed interaction network-deep neural network model. Finally, considering that learners and different interactive courses play different roles in the final recommendation and prediction results, an attention mechanism is introduced. The accuracy, recall rate, and F1 value of the proposed method are 0.851, 0.856, and 0.853, respectively, when the length of the recommendation list K is 35. Consequently, the proposed strategy outperforms the comparison model in terms of recommending customized course resources.