• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.1-17
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• 2023

With the recent rapid development of ICT(Information and Communication Technology) and the popularization of digital devices, the size of the online market continues to grow. As a result, we live in a flood of information. Thus, customers are facing information overload problems that require a lot of time and money to select products. Therefore, a personalized recommender system has become an essential methodology to address such issues. Collaborative Filtering(CF) is the most widely used recommender system. Traditional recommender systems mainly utilize quantitative data such as rating values, resulting in poor recommendation accuracy. Quantitative data cannot fully reflect the user's preference. To solve such a problem, studies that reflect qualitative data, such as review contents, are being actively conducted these days. To quantify user review contents, text mining was used in this study. The general CF consists of the following three steps: user-item matrix generation, Top-N neighborhood group search, and Top-K recommendation list generation. In this study, we propose a recommendation algorithm that applies an extended similarity measure, which utilize quantified review contents in addition to user rating values. After calculating review similarity by applying TF-IDF, Word2Vec, and Doc2Vec techniques to review content, extended similarity is created by combining user rating similarity and quantified review contents. To verify this, we used user ratings and review data from the e-commerce site Amazon's "Health and Personal Care". The proposed recommendation model using extended similarity measure showed superior performance to the traditional recommendation model using only user rating value-based similarity measure. In addition, among the various text mining techniques, the similarity obtained using the TF-IDF technique showed the best performance when used in the neighbor group search and recommendation list generation step.

• International Commerce and Information Review
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• v.4 no.2
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• pp.155-175
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• 2002

본 연구는 전자무역을 혁신수용의 관점에서 기술하고 있다. 관련 문헌의 고찰을 통해 천자무역 특성요인과 수용자(무역업체) 특성요인을 도출하고, 이를 바탕으로 연구모형의 개발 및 연구가설을 설정하였다. 연구가설은 인터넷리서치를 통해 수집된 자료를 다중회귀분석기법을 이용하여 검정하였다. 그 결과를 요약하면 다음과 같다. 첫째, 전자무역의 특성변수들(지각된 유용성/편의성/위험성)이 전자무역 수용도에 미치는 영향관계를 검증한 결과, 지각된 유용성과 지각된 편의성이 높을수록 전자무역 수용도가 높은 것으로 나타났다. 둘째, 혁신 수용자로서 무역업체의 특성변수들(혁신성향, 정보인프라 성숙도)이 전자무역 수용도에 미치는 영향관계를 검증한 결과, 혁신성향과 정보인프라 성축도 모두 전자무역 수용도에 긍정적인 영향을 미치는 것으로 나타났다. 이는 혁신성향이 높을수록 그리고 정보인프라가 성숙된 업체일수록 전자무역 수용도가 높게 나타난다는 일반적인 견해와 일치되는 결과이다. 셋째, 두 특성변수들(전자무역 특성, 무역업체 특성)간의 상황적 관계에서는 혁신성향이 낮은 무역업체일수록 전자무역의 정보위험성을 높게 인식하여 천자무역 수용(현재 활용정도와 지속적 이용의도)을 거부할 가능성이 높게 나타났다. 또한 정보인프라 성숙도가 낮은 무역업체일수록 정보위험성을 높게 인식하여 전자무역 수용을 거부할 가능성이 높게 나타났다. 넷째, 전자무역을 통한 수출입 경험여부에 따른 전자무역의 향후 이용의도와의 관계를 분석한 결과 무경험업체의 경우는, 혁신성향이 높고 정보인프라가 성숙된 무역업체일수록 향후 이용의도가 높은 것으로 나타났다. 또한 정보인프라 성숙도가 낮은 무역업체일수록 전자무역의 편의성을 낮게 인식하여 전자무역 수용을 거부할 가능성이 높게 나타났다. 유경험업체의 경우는 전자무역에 대한 향후 이용의도가 높은 무역업체일수록 전자무역의 편의성을 오히려 부정적으로 평가하는 경향이 드러났는데, 이러한 현상은 유경험업체가 인식하는 편의성에 대한 기대수준이 무경험업체에 비해 높기 때문인 것으로 나타났다. 또한 혁신성향이 높은 무역 업체가 향후 전자무역을 지속적으로 이용하기 위해서는 전자무역의 편의성을 더 높게 요구하는 것으로 나타냈다. 전자무역의 수용도를 높이기 위해서는 전자무역의 특성요인들에 대한 잠재적 수용자의 태도변화를 파악하는 것도 중요하지만, 수용자 집단의 특성에 맞는 상황적 전략수립이 동시에 필요하다. 그러한 의미에서 본 논문은 전자무역 수용 촉진 전략을 수용자 집단의 특성별로 그리고 상황적으로 수립할 수 있는 기초자료를 제공할 수 있을 것으로 기대된다.

• Journal of the Korean Society for information Management
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• v.27 no.1
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• pp.7-24
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• 2010

This study tried to develop the confidence indicators and evaluation factors of credibility according to the types of online information by nationwide large scale online survey. The main results are summarized as follows: i) confidence indicators by types of online information: information on news sites(72.553), financial companies(68.894), government sites(67.938), cafe(66.464), portal sites(65.001), collective intelligence sites(63.489), nonprofit organization (63.392), company/corporation sites(59.789), blog(59.066), online community sites(55.609), e-commerce sites(55.118), mini-homepage(50.695). ii) 'Widely known site' or 'famous site' is the most important factor for all types of online information. User opinions like as posting or comment are major factors for sites of cafe, blog, mini-homepage, online community, collective intelligence etc. and 'name recognition' and 'reputation' are main factors for site of financial company, corporation, government, nonprofit organization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.4
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• pp.980-985
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• 2008

The environment of IT is, currently, on its developing process to the period of web 2.0 and mashup which not only enable computer and internet to be utilized like the water or the air, but also be a new motivating force for its advance. One of the biggest changes of the industry that lies ahead is consolidated transportation. However, no party outstands as the leading party for nationwide improvement of logistics, nor does the right analysis and design for it. Therefore, successful nationwide logistics model is yet to exist. This study provides individual parties, which consider consolidated transportation model as their implementation and design of the framework, with instructions for logistics information system so that they could be competitive in the market. It also helps companies collect user requirements for logistics information system consolidated transportation, and utilize it for its development. Finally, the study provides a implementation and design of pilot system for consolidated transportation model.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.641-650
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• 2001

In recent years, e-Commerce is very active on the Internet, especially the World Wide Web along with the popularization of Internet using high-speed networks. Especially, Electronic Software Distribution(ESD) is widely being focused as one of the popular researches. However, the existing models of ESD lack substantial illegal copy protection or copyright protection as they have the shortcomings of guaranteeing anonymity of users. This study suggests an ESD protocol that guarantees substantial copyright protection and anonymity based on the Public Key Infrastrncture(PKl). The suggested method does not give the information of a buyer who doesn't want to reveal to a seller, and protects illegal copy and distribution as well. When it happens that illegal copies are in circulation, this method provides a device to trace back its original distributor so that it helps protect the copyright. In addition, it provides more convenient environment to the user by not using the methods of serial number input and extra installation to use.

• Journal of information and communication convergence engineering
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• v.18 no.2
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• pp.123-131
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• 2020

After observing that most tourists plan to complete their visits to multiple cultural heritage sites within one day, we surmised that for many museum visitors, the foremost thought is with regard to the amount of time is to be spent at each location and how they can maximize their enjoyment at a site while still balancing their travel itinerary? Recommendation systems in e-commerce are built on knowledge about the users' previous purchasing history; recommendation systems for museums, on the other hand, do not have an equivalent data source available. Recent solutions have incorporated advanced technologies such as algorithms that rely on social filtering, which builds recommendations from the nearest identified similar user. Our paper proposes a different approach, and involves providing dynamic recommendations that deploy social filtering as well as content-based filtering using term frequency-inverse document frequency. The main challenge is to overcome a cold start, whereby no information is available on new users entering the system, and thus there is no strong background information for generating the recommendation. In these cases, our solution deploys statistical methods to create a recommendation, which can then be used to gather data for future iterations. We are currently running a pilot test at Chao Samphraya national museum and have received positive feedback to date on the implementation.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.222-226
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• 2010

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through these reviews. However, since online shopping malls do not provide ranking results, it is not easy for users to read all the relevant review documents effectively. Product reviews include subjective and emotional opinions. Thus, the review search is different from the general web search in terms of ranking strategy. In this paper, we propose an effective method of ranking the reviews that can reflect user's intention by using opinion mining techniques. The proposed method analyzes product reviews with query words, and sentimental polarity of subjective opinions. Through diverse experiments, we show that our proposed method outperforms conventional ones.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.809-818
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• 2009

Collaborative filtering approach predicts the preference of active user about specific items transacted on the e-commerce by using others' preference information. To improve the prediction accuracy through collaborative filtering approach, it must be needed to gain enough preference information of users' for predicting preference. But, a bit much information of users' preference might wrongly affect on prediction accuracy, and also too small information of users' preference might make bad effect on the prediction accuracy. This research suggests the method, which decides suitable numbers of neighbor users for applying collaborative filtering algorithm, improved by existing k nearest neighbors selection methods. The result of this research provides useful methods for improving the prediction accuracy and also refines exploratory data analysis approach for deciding appropriate numbers of nearest neighbors.

• Journal of Information Technology Application
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• v.3 no.1
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• pp.1-21
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• 2001

The explosive growth of the Internet and the increasing popularity of the World Wide Web have generated significant interest in the development of electronic commerce in a global online marketplace. The rapid adoption of the Internet as a commercial medium is rapidly expanding the necessity of Web advertisement as a new communication channel. if proper Web advertisement could be suggested to the right user, then effectiveness of Web advertisement will be raised and it will help company to earn more profit. So, this article describes a personalized advertisement technique as a part of intelligent customer services for an electronic newspaper provide. Based on customers history of navigation on the electronic newspapers pages, which are divided into several sections such as politics, economics, sports, culture, and so on, appropriate advertisements (especially, banner ads) are chosen and displayed with the aid of machine learning techniques, when customers visit to the site. To verify feasibility of the technique, an application will be made to one of the most popular e-newspaper publishing company in Korea.

• Journal of KIISE:Databases
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• v.34 no.5
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• pp.389-395
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• 2007

Collaborative filtering is a popular technique for information filtering to reduce information overload and widely used in application such as recommender system in the E-commerce domain. Collaborative filtering systems collect human ratings and provide Predictions based on the ratings of other people who share the same tastes. The quality of predictions depends on the number of items which are commonly rated by people. Therefore, it is difficult to apply pure collaborative filtering algorithm directly to dynamic collections where items are constantly added or removed. In this paper we suggest a method for managing dynamic collections. It creates taste space for items using a technique called Singular Vector Decomposition (SVD) and maintains clusters of core items on the space to estimate relevance of past and future items. To evaluate the proposed method, we divide database of user ratings into those of old and new items and analyze predicted ratings of the latter. And we experimentally show our method is efficiently applied to dynamic collections.