• Information Systems Review
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• v.25 no.1
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• pp.27-46
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• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• International Journal of Contents
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• v.17 no.3
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• pp.30-37
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• 2021

This study aimed to determine the effects of simulation learning program using SBAR (Situation, Background, Assessment, Recommendation) techniques on undergraduate nursing students' clinical judgment and communication skills. A quasi-experimental research design (one-group pretest-posttest design) was used in this study. The participants included 88 students from a nursing college. There were statistically significant differences in clinical judgment, communication clarity, and communication confidence between pre-simulation learning using SBAR and post (t=10.32, p<.0001; t=6.05, p=<.0001; t=7.42, p=<.0001). The simulation learning program using SBAR was found to improve nursing students' clinical judgment as well as clarity and confidence in interprofessional communication.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.383-385
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• 2017

For the purpose of providing high data rate real-time services, radio transmission technologies for ship ad-hoc network based on the Recommendation ITU-R 1842-1 are designed. In order to increase the link throughput of real-time services, in this paper, we investigate the performance of the SANET with the spatial transmit diversity techniques are employed. Based on the analysis of the packet error rate and throughput, we select the efficient multiple antenna schemes for SANET to improve the link reliability.

• Knowledge Management Research
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• v.21 no.2
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• pp.177-196
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• 2020

Since the adoption of K-IFRS(Korean International Financial Reporting Standards), the amount of financial footnotes has been increased. However, due to the stereotypical phrase and the lack of conciseness, deriving the core information from footnotes is not really easy yet. To propose a solution for this problem, this study tried financial footnote analysis for financial ratio predictions based on text-mining techniques. Using the financial statements data from 2013 to 2018, we tried to predict the earning per share (EPS) of the following quarter. We found that measured prediction errors were significantly reduced when text-mined footnotes data were jointly used. We believe this result came from the fact that discretionary financial figures, which were hardly predicted with quantitative financial data, were more correlated with footnotes texts.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.331-339
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• 2004

The preferences of customers change over time. However, existing collaborative filtering (CF) systems are static, since they only incorporate information regarding whether a customer buys a product during a certain period and do not make use of the purchase sequences of customers. Therefore, the quality of the recommendations of the typical CF could be improved through the use of information on such sequences. In this paper, we propose a new methodology for enhancing the quality of CF recommendation that uses customer purchase sequences. The proposed methodology is applied to a large department store in Korea and compared to existing CF techniques. Various experiments using real-world data demonstrate that the proposed methodology provides higher quality recommendations than do typical CF techniques, with better performance, especially with regard to heavy users.

• Proceedings of the Korean Society for Information Management Conference
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• 2017.08a
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• pp.23-32
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• 2017

Music therapy has been proved to be effective in treatment of diseases such as Alzheimer's disease. Many studies have investigated the effect of music therapy techniques on symptoms of a given disease but there has been no efforts in classifying those studies by specific symptoms of diseases, although patients, caregivers and music therapists have difficulty in discovering documents that they need to treat certain diseases. Thus, in the study, we propose a method to group music therapy-related publications by the music therapy techniques mainly used for a given disease. We expect that it will help music therapists and patients to find papers to help them to cure a specific disorder.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.260-263
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• 2023

Considering the growth of blockchain technology, the research aims to transform the efficiency of recommending optimal coffee suppliers within the complex supply chain network. This transformation relies on the extraction of vital transactional data and insights from stakeholders, facilitated by the dynamic interaction between the application interface (e.g., Rest API) and the blockchain network. These extracted data are then subjected to advanced data processing techniques and harnessed through machine learning methodologies to establish a robust recommendation system. This innovative approach seeks to empower users with informed decision-making abilities, thereby enhancing operational efficiency in identifying the most suitable coffee supplier for each customer. Furthermore, the research employs data visualization techniques to illustrate intricate clustering patterns generated by the K-Means algorithm, providing a visual dimension to the study's evaluation.

• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2516-2523
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• 2024

This study aims to validate the dosimetric characteristics of Low Dose Rate (LDR) I-125 source Geant4-based Monte Carlo code. According to the recommendation of the American Association of Physicists in Medicine (AAPM) task group report (TG-43), the dosimetric parameters of a new brachytherapy source should be verified either experimentally or theoretically before clinical procedures. The simulation studies are very important since this procedure delivers a high dose of radiation to the tumor with only a minimal dose affecting the surrounding tissues. GEANT4 Monte Carlo simulation toolkit associated brachytherapy example was modified, adapted and several updated techniques have been developed to facilitate and smooth radiotherapy techniques. The great concordance of the current study results with the consensus data and with the results of other MC based studies is promising. It implies that Geant4-based Monte Carlo simulation has the potential to be used as a reliable and standard simulation code in the field of brachytherapy for verification and treatment planning purposes.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.123-130
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• 2019

One of the most commonly used methods of web recommendation techniques is collaborative filtering. Many studies on collaborative filtering have suggested ways to improve accuracy. This study proposes a method of movie recommendation using Word2Vec and an ensemble convolutional neural networks. First, in the user, movie, and rating information, construct the user sentences and movie sentences. It inputs user sentences and movie sentences into Word2Vec to obtain user vectors and movie vectors. User vectors are entered into user convolution model and movie vectors are input to movie convolution model. The user and the movie convolution models are linked to a fully connected neural network model. Finally, the output layer of the fully connected neural network outputs forecasts of user movie ratings. Experimentation results showed that the accuracy of the technique proposed in this study accuracy of conventional collaborative filtering techniques was improved compared to those of conventional collaborative filtering technique and the technique using Word2Vec and deep neural networks proposed in a similar study.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.