• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.1-18
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• 2022

As digital technology converges into the e-commerce market across industries, online transactions have activated, and the use of online has increased. With the recent spread of infectious diseases such as COVID-19, this market flow is accelerating, and various product information can be provided to customers online. Providing a variety of information provides customers with various opportunities but causes difficulties in decision-making. The recommendation system can help customers to make a decision more effectively. However, the previous research on recommendation systems is limited to only quantitative data and does not reflect detailed factors of products and customers. In this study, we propose an intelligent recommendation system that quantifies the attributes of products and customers by applying text mining techniques to qualitative data based on online reviews and integrates the existing objective indicators of total star rating, sentiment, and emotion. The proposed integrated recommendation model showed superior performance to the overall rating-oriented recommendation model. It expects the new business value to be created through the recommendation result reflecting detailed factors of products and customers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3798-3814
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• 2022

Social recommendation algorithm can alleviate data sparsity and cold start problems in recommendation system by integrated social information. Among them, matrix-based decomposition algorithms are the most widely used and studied. Such algorithms use dot product operations to calculate the similarity between users and items, which ignores user's potential preferences, reduces algorithms' recommendation accuracy. This deficiency can be avoided by a metric learning-based social recommendation algorithm, which learns the distance between user embedding vectors and item embedding vectors instead of vector dot-product operations. However, previous works provide no theoretical explanation for its plausibility. Moreover, most works focus on the indirect impact of social friends on user's preferences, ignoring the direct impact on user's rating preferences, which is the influence of user rating preferences. To solve these problems, this study proposes a user bias drift social recommendation algorithm based on metric learning (BDML). The main work of this paper is as follows: (1) the process of introducing metric learning in the social recommendation scenario is introduced in the form of equations, and explained the reason why metric learning can replace the click operation; (2) a new user bias is constructed to simultaneously model the impact of social relationships on user's ratings preferences and user's preferences; Experimental results on two datasets show that the BDML algorithm proposed in this study has better recommendation accuracy compared with other comparison algorithms, and will be able to guarantee the recommendation effect in a more sparse dataset.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.9
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• pp.67-76
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• 2018

The improvements of G-SEED standards was studies in this research by comparing the composition, rating scores, and standards, including categories, issues and criteria of BREEAM in the UK. In this research, it was found that both certification systems had very similar composition and rating system, however, there was slight difference in the points of emphasizing the standards due to the differences in social awareness of green buildings in each country. As a result of the detailed comparison of both standards, in addition, it was found that almost half of criteria had different evaluation approaches such as evaluation methods and scopes between both standards even though they had same evaluation objectives; some of the differences were due to the social or cultural differences between the countries, and the others were not. Through the research, we were able to derive some criteria that need for improvements of G-SEED considering the domestic conditions in Korea.

• Journal of the Korea Institute of Building Construction
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• v.23 no.4
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• pp.453-464
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• 2023

This research conducts a comparative analysis of the perceived importance of advancing zero energy building certification from the viewpoints of two major stakeholders - designers and building energy assessors. Both groups prioritized the importance of policy, technology, education, incentives, and promotion respectively. For designers, enhancing energy efficiency standards, developing a skilled energy workforce, and implementing an office registration system emerged as critical factors in invigorating the certification process. The findings suggest potential avenues for the government to formulate realistic strategies for boosting the certification activity.

• The Korean Journal of Pesticide Science
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• v.7 no.2
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• pp.123-130
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• 2003

The Environmental Impact Quotient(EIQ) has been used to organize the extensive toxicological and soil residue data available on some fruit vegetables pesticides into a usable form for field use. It addresses a majority of the environmental concerns that are encountered in agricultural systems including farm worker, consumer, ecological and environmental safety. The EIQ made use of the rating system by using the EIQ equation. The EIQ of pesticides registered for oriental melon was higher than that of other in terms of farm worker, consumer and environmental safety. Pesticides registered for red pepper showed highest EIQ in ecosystem. The EIQ decreased in order of insecticide> fungicide> herbicide> plant growth regulator. The environment Impact Quotient for the pesticides registered in fruit and vegetables decrease gradually.

• The Journal of Information Systems
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• v.15 no.3
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• pp.175-186
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• 2006

최근 정보기술(IT; Information Technology) 및 네트워크 기술의 발달은 기업사회의 의사결정 패턴에 큰 변화를 주고 있다. 특히 글로벌 정치경제 환경이 급변함에 따라 기업들의 의사결정은 보다 빠른 피드백(feedback loop)을 요구하고 있어 과거의 정확성을 중심의 패턴에 변화된 정보의 시기적 절성(timely information)이 크게 강조되고 있다. 본 논문에서는 이러한 첨단기술사회에서 빠르게 의견수렴을 할 수 있는 기술적인 프레임워크를 구축하였다. 본 시스템은 현대사회의 주요한 경제 및 재무의사결정 구조(infrastructure)인 신용평가(credit rating)제도를 웹 기반 시스템으로 구현함으로서 정보의 시기적절성과 현재성을 높이는 의사결정지원시스템을 시현하였다.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.576-581
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• 2007

The purpose of this paper is to strengthen trust on the automated collaborative filtering system. Automated collaborative filtering system is quickly becoming a popular technique for recommendation system. This elaborative methodology contributes for reducing information overload and the result becomes index of users' preference. In addition, it can be applied to various industries in various fields. After it collaborative filtering system was developed, many researches are executed to enhance credibility and to apply in various fields. Among these diverse systems, collaborative filtering system which uses Pearson correlation coefficient is most common in many researches. In this paper, we proposed new process diagram of collaborative filtering algorithm and new factors which should improve the credibility of system. In addition, the effects and relationships are also tested.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• Smart Structures and Systems
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• v.20 no.2
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• pp.139-150
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• 2017

The structural strain plays a significant role in structural condition assessment of in-service bridges in terms of structural bearing capacity, structural reliability level and entire safety redundancy. Therefore, it has been one of the most important parameters concerned by researchers and engineers engaged in structural health monitoring (SHM) practices. In this paper, an SHM system instrumented on the Jiubao Bridge located in Hangzhou, China is firstly introduced. This system involves nine subsystems and has been continuously operated for five years since 2012. As part of the SHM system, a total of 166 fiber Bragg grating (FBG) strain sensors are installed on the bridge to measure the dynamic strain responses of key structural components. Based on the strain monitoring data acquired in recent two years, the strain-based structural condition assessment of the Jiubao Bridge is carried out. The wavelet multi-resolution algorithm is applied to separate the temperature effect from the raw strain data. The obtained strain data under the normal traffic and wind condition and under the typhoon condition are examined for structural safety evaluation. The structural condition rating of the bridge in accordance with the AASHTO specification for condition evaluation and load and resistance factor rating of highway bridges is performed by use of the processed strain data in combination with finite element analysis. The analysis framework presented in this study can be used as a reference for facilitating the assessment, inspection and maintenance activities of in-service bridges instrumented with long-term SHM system.

• Information Systems Review
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• v.20 no.4
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• pp.79-96
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• 2018

This study started by focusing on the internalization of the technology appraisal model into the credit rating model to increase the discriminative power of the credit rating model not only for SMEs but also for all companies, reflecting the items related to the financial stability of the enterprises among the technology appraisal items. Therefore, it is aimed to verify whether the technology appraisal model can be applied to identify high-stability SMEs in advance. We classified companies into industries (manufacturing vs. non-manufacturing) and the age of company (initial vs. non-initial), and defined as a high-stability company that has achieved an average debt ratio less than 1/2 of the group for three years. The C5.0 was applied to verify the discriminant power of the model. As a result of the analysis, there is a difference in importance according to the type of industry and the age of company at the sub-item level, but in the mid-item level the R&D capability was a key variable for discriminating high-stability SMEs. In the early stage of establishment, the funding capacity (diversification of funding methods, capital structure and capital cost which taking into account profitability) is an important variable in financial stability. However, we concluded that technology development infrastructure, which enables continuous performance as the age of company increase, becomes an important variable affecting financial stability. The classification accuracy of the model according to the age of company and industry is 71~91%, and it is confirmed that it is possible to identify high-stability SMEs by using technology appraisal items.