• The Journal of Korean Association of Computer Education
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• v.6 no.3
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• pp.27-36
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• 2003

Rccommender systems are being used by an ever-increasing number of E-Commerce sites to help consumers find products to purchase. Recommender Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different user' tastes. However, despite enormous interest in recommender systems, both the number of available published techniques and information about their performance are limited. In this paper. we design and implement an M-Commerce recommendation systems using the past buying behavior of the consumer, consumer information, and association rule mining.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.1-24
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• 2016

With the innovation of information technology, non-face-to-face robo advisor with high accessibility and convenience is spreading. The current robot advisor recommends appropriate investment products after understanding the investment propensity based on the structured data entered directly or indirectly by individuals. However, it is an inconvenient and obtrusive way for financial consumers to inquire or input their own subjective propensity to invest. Hence, this study proposes a way to deduce the propensity to invest in unstructured data that customers voluntarily exposed during consultation or online. Since prediction performance based on unstructured document differs according to the characteristics of text, in this study, classification algorithm optimized for the characteristic of text left by financial consumers is selected by performing prediction performance evaluation of various learning discrimination algorithms and proposed an intelligent method that automatically recommends investment products. User tests were given to MBA students. After showing the recommended investment and list of investment products, satisfaction was asked. Financial consumers' satisfaction was measured by dividing them into investment propensity and recommendation goods. The results suggest that the users high satisfaction with investment products recommended by the method proposed in this paper. The results showed that it can be applies to non-face-to-face robo advisor.

• The Journal of Bigdata
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• v.7 no.1
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• pp.187-192
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• 2022

Many schools are trying to improve students' competencies through many subjects and non-curricular activities, each students has different goals and different activities to prepare for employment. Accordingly, it is difficult to determine whether the programs offered in a comprehensive and comprehensive manner in the existing subject and non-curricular subjects systems are actually suitable for students, so it is necessary to introduce a personalized system. In this study, a method was proposed to classify non-departmental subjects that are uniformly provided to all students of Chungbuk National University by grade level and department. In addition, three types of collaborative filtering models are implemented using the evaluation score of students who participated in the non-curricular program, and personalized recommendations are proposed with the most accurate model by comparing performance.

• Korean Journal of Acupuncture
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• v.40 no.4
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• pp.206-211
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• 2023

Objectives : Needle sickness is one of the adverse events of acupuncture, although substantial adverse effects during a routine acupuncture treatment seem to be highly unusual. In this work, we propose that an acupuncture-related vasovagal response resembles needle sickness during acupuncture therapy. Methods : In this article, we discussed the general characteristics of vasovagal syncope and went into more detail on vasovagal syncope in people who have a fear of blood injection and injury. We also offer a recommendation for the prevention and management of vasovagal syncope brought on by acupuncture. Results : The vasovagal reaction related to acupuncture is closely associated with needle sickness. The prevention can be done using PEACHES (position, experience, anxiety, constitution, hydration, environment, symptom recognition) principles. The management should be conducted using the RIPCORD (recognize, initiate, position, communicate, order treatments, reassess, document) techniques. Conclusions : It is important to comprehend the characteristics of needle sickness as a vasovagal reaction related to acupuncture. According to the recommendation, practitioners should effectively prevent and manage needle sickness.

• Korean Journal of Radiology
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• v.24 no.1
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• pp.22-30
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• 2023

Ultrasonography (US) is a primary imaging modality for diagnosing nodular thyroid disease and has an essential role in identifying the most appropriate management strategy for patients with nodular thyroid disease. Standardized imaging techniques and reporting formats for thyroid US are necessary. For this purpose, the Korean Society of Thyroid Radiology (KSThR) organized a task force in June 2021 and developed recommendations for standardized imaging technique and reporting format, based on the 2021 KSThR consensus statement and recommendations for US-based diagnosis and management of thyroid nodules. The goal was to achieve an expert consensus applicable to clinical practice.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.205-213
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• 2010

This study uses the FP-tree algorithm, one of the mining techniques. This study is an attempt to suggest a new recommended system using a modified FP-tree algorithm which yields an association rule based on frequent 2-itemsets extracted from the transaction database. The modified recommended system consists of a pre-processing module, a learning module, a recommendation module and an evaluation module. The study first makes an assessment of the modified recommended system with respect to the precision rate, recall rate, F-measure, success rate, and recommending time. Then, the efficiency of the system is compared against other recommended systems utilizing the sequential pattern mining. When compared with other recommended systems utilizing the sequential pattern mining, the modified recommended system exhibits 5 times more efficiency in learning, and 20% improvement in the recommending capacity. This result proves that the modified system has more validity than recommended systems utilizing the sequential pattern mining.

• Science of Emotion and Sensibility
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• v.10 no.1
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• pp.37-47
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• 2007

Color planning system can be treated as a decision support system which includes both the recommendation of main color and harmonious colors. In this paper, we propose techniques that are useful to enhance the harmonious color recommendation with the main color selected by user. In order to reflect the knowledge about suggestion of harmonious colors, we use Ostwald color harmony theory, that is very systematical and easy to implement. Actually, Ostwald color space is similar to HMMD color model in MPEG-7. Due to the similarity between two color spaces, Ostwald color space can be represented as a virtual HMMD color space. Accordingly, we propose a technique to align the HMMD color space with Ostwald color space, thereby it is capable of enhancing a performance to search the harmonious colors according to Ostwald harmony theory. For recommendation of delicate and more exquisite harmonious colors in equal hue plane, we made the virtual color space continuous. The system can recommend various harmonious colors according to Ostwald color harmony. He(she) can select harmonious colors among the suggestions from the system.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.287-308
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• 2022

The explosive growth of cryptocurrency, led by Bitcoin has emerged as a major issue in the financial market recently. As a result, interest in cryptocurrency investment is increasing, but the market opens 24 hours and 365 days a year, price volatility, and exponentially increasing number of cryptocurrencies are provided as risks to cryptocurrency investors. For that reasons, It is raising the need for research to reduct investors' risks by dividing cryptocurrency which is not suitable for recommendation. Unlike the previous studies of maximizing returns by simply predicting the future of cryptocurrency prices or constructing cryptocurrency portfolios by focusing on returns, this paper reflects the tendencies of investors and presents an appropriate recommendation method with interpretation that can reduct investors' risks by selecting suitable Altcoins which are recommended using Apriori algorithm, one of the machine learning techniques, but based on the similarity and association rules of Bitocoin.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.357-359
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• 2023

This paper proposes a hybrid recommendation system (RS) model that overcomes the limitations of traditional approaches such as data sparsity, cold start, and scalability by combining collaborative filtering and context-aware techniques. The objective of this model is to enhance the accuracy of recommendations and provide personalized suggestions by leveraging the strengths of collaborative filtering and incorporating user context features to capture their preferences and behavior more effectively. The approach utilizes a novel method that combines contextual attributes with the original user-item rating matrix of CF-based algorithms. Furthermore, we integrate k-mean++ clustering to group users with similar preferences and finally recommend items that have highly rated by other users in the same cluster. The process of partitioning is the use of the rating matrix into clusters based on contextual information offers several advantages. First, it bypasses of the computations over the entire data, reducing runtime and improving scalability. Second, the partitioned clusters hold similar ratings, which can produce greater impacts on each other, leading to more accurate recommendations and providing flexibility in the clustering process. keywords: Context-aware Recommendation, Collaborative Filtering, Kmean++ Clustering.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.563-565
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• 2021

The most important feature of the recently increasing content providing service is that the amount of content increase over time is very large. Accordingly, the importance of user curation is increasing, and various techniques are used to implement it. In this paper, among the techniques for video recommendation, the analysis technique using voice data and subtitles and the video comparison technique based on keyframe extraction are compared with the results of implementing and applying the video content of real big data. In addition, through the comparison result, a video content environment to which each analysis technique can be applied is proposed.