• The Journal of Society for e-Business Studies
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• v.14 no.4
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• pp.19-33
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• 2009

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 the reviews. Product Reviews are results expressing customer's sentiments and thus are divided into positive reviews and negative ones. However, as the number of reviews in on-line shopping increases, it is inefficient or sometimes impossible for users to read all the relevant review documents. In this paper, we present a sentiment analysis algorithm for automatically classifying subjective opinions of customer's reviews using opinion mining technology. The proposed algorithm is to focus on product reviews of on-line shopping, and provides summarized results from large product review data by determining whether they are positive or negative. Additionally, this paper introduces an automatic review analysis system implemented based on the proposed algorithm, and also present the experiment results for verifying the efficiency of the algorithm.

• Proceedings of the Korean Society of Computer Information Conference
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• 2011.06a
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• pp.377-379
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• 2011

기존의 오프라인보다 인터넷 쇼핑몰의 고객유지가(retention)가 중요해지는 상황에서, 기존의 백화점에서 쇼핑하는 쇼핑객들이 중요하게 생각하는 개념과 인터넷 쇼핑을 이용하는 인터넷 이용자들이 중요하게 생각하는 개념을 비교하면서 지각된 가치(perceived value)가 고객만족(customer satisfaction), 신뢰(trust)에 미치는 영향에 관한 구조적 관계를 설정하고 온라인과 오프라인쇼핑몰에 대해 비교 분석한 결과는 다음과 같다. 인터넷쇼핑몰의 경우에는 고객이 만족하면 신뢰감이 생긴다. 오프라인쇼핑몰에서는 고객만족하면 신뢰의 중요성이 낮게 나타난다. 인터넷 기업과 오프라인쇼핑몰 기업 측면에서 고객만족과 신뢰를 확보할 수 있는 전략적 가이드를 제공한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.05a
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• pp.500-503
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• 2018

최근 스마트폰의 보급률이 높아지면서 인터넷 쇼핑몰의 접근성이 용이해지고 있고 그로 인해 사용자들의 인터넷 쇼핑의 이용이 보편적이게 되었다. 그 중 여성 의류 분야는 많은 비중을 차지하고 있으며 현재도 꾸준히 성장하고 있는 추세이다. 많은 여성 소비자들은 개인의 취향에 맞는 의류들을 추천받기를 원한다. 본 논문에서는 협업 필터링에서 발생하는 cold start 문제를 이름, 나이, 선호 스타일, 자주 사용하는 쇼핑몰 등 개인 성향을 이용하여 해결하는 협업 필터링과 개인 성향을 이용한 여성 의류 쇼핑몰 추천 시스템을 제안한다.

• The Journal of the Korea Contents Association
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• v.6 no.11
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• pp.25-37
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• 2006

The purpose of this study examines the interrelationships among antecedents and consequences of privacy concern on the online-shopping mall. Based on relevant literature review, a customer's attitude toward direct marketing, a customer's desire to information control, and a customer's prediction of negative effect as antecedents that affect the privacy concern. Also, consequences are a firm's reputation and a customer's purchase experience. Then related hypotheses were tested using data from 165 online shopping mall customer. The results for empirical analysis are as follows; 1) a customer's attitude toward direct marketing affected negatively the privacy concern, 2) a customer's desire to information control and a customer's prediction of negative effect affected positively the privacy concern, 3) a firm's reputation negatively related to the privacy concern, 4) a customer's purchase experience positively related to a firm's reputation.

• 베이커리
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• no.5 s.382
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• pp.114-115
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• 2000

• Product Safety
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• no.12 s.169
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• pp.22-25
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• 2007

• 보일러설비
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• s.114
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• pp.101-104
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• 2003

• The Journal of Bigdata
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• v.7 no.2
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• pp.153-172
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• 2022

Online shopping has transformed and rapidly grown the entire market at the forefront of wholesale and retail services as an effective solution to issues such as digital transformation and social distancing policy (COVID-19 pandemic). Small business owners, who form the majority at the center of the online shopping industry, are constantly collecting policy changes and market trend information to overcome these problems and use them for marketing and other sales activities in order to overcome these problems and continue to grow. Objective and refined information that is more closely related to the business is also needed. Therefore, in this paper, through the collection and analysis of big data information, which is the core technology of digital transformation, key variables are set in product classification, sales trends, consumer preferences, and review information of online shopping malls, and a method of using them for competitor comparison analysis and business sustainability evaluation has been prepared and we would like to propose it as a service. If small and medium-sized businesses can benchmark competitors or excellent businesses based on big data and identify market trends and consumer tendencies, they will clearly recognize their level and position in business and voluntarily strive to secure higher competitiveness. In addition, if the sustainable growth of the online shopping mall operator can be confirmed as an indicator, more efficient policy establishment and risk management can be expected because it has an improved measurement method.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.171-182
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• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.30-32
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• 1999

인터넷의 증가로 온라인 상점들의 수는 매우 빠르게 증가하고 있다. 상점의 수가 늘어날수록 사용자가 이러한 상점들에서 원하는 정보를 찾는 일은 쉽지 않다. 사용자의 어려움을 줄이고자 여러 쇼핑몰의 정보들을 통합해서 보여주는 전자상거래 통합 시스템들이 생겨나고 있지만, 새로운 쇼핑몰이 추가될 때마다 관리자가 추가되는 쇼핑몰의 정보를 추출하기 위한 규칙이나, Ontology등을 수동으로 만들거나 확장해야 하기 때문에 사람이 소비해야 하는 시간과 노력이 많고, 시스템을 관리하는 사람에 다라 정보추출의 정확도도 다르다. 따라서 사람이 소비하는 시간을 줄이고, 좀 더 정확한 정보추출을 위해 쇼핑몰마다 만들어야 하는 규칙과 그러한 규칙 생성에 필요한 Ontology를 자동으로 생성하는 방법과 이 방법에서 요구되는 사용자의 입력을 최소한 줄인 인터페이스 에이전트를 제안한다.