• The Journal of Society for e-Business Studies
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• v.11 no.3
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• pp.35-51
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• 2006

Information visualization plays an important role to provide a conceptual comprehension of the information and should be adjusted and featured to reflect the characteristics of the domain to which it is applied. Product searching in e-Commerce is not an exception. In this paper, we present visualization techniques that are specified for effectively browsing the results of product searching. We considered visualization techniques including MapNet and Cluster Map and modified them to reflect the characteristics of the product domain. We consider two types of search queries: category searching and product property searching, and make it possible to include more detailed product semantics in their search criteria. We also provide a query refinement mechanism so that even users with rack of understanding the products may rewrite their queries for better results.

• Journal of the Korean Society of Clothing and Textiles
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• v.44 no.6
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• pp.1053-1069
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• 2020

This study examined the sales pattern relationship with respect to product attributes to propose sales forecasting for fashion products. We analyzed 537 SKU sales data of T-shirts in the domestic sports brand using SAS program. The sales pattern of fashion products fluctuated and were influenced by exogenous factors; therefore, we removed the influence of exogenous factors found to be price discounts and holiday effects as a result of regression analysis. In addition, it was difficult to predict sales using the sales patterns of the same product since fashion products were released as new products every year. Therefore, the forecasting model was proposed using sales patterns of related product attributes when attributes were considered descriptive variables. We classified sales patterns using K-means clustering in order to explain the relationship between sales patterns and product attributes along with creating a decision tree classifier using attributes as input and sales patterns as output. As a result, the sales patterns of T-shirts were clustered into six types that featured the characteristic shape of peak and slope. It was also associated with the combination of product attributes and their values in regards to the proposed sales pattern prediction model.

• 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.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.112-113
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• 2010

최근 오피니언 마이닝 관련 연구 중 감정 분류에 대한 관심이 높아지면서 많은 연구가 진행되고 있다. 기존 영어권 연구에서 제시되어온 방법은 한국어 상품평에 적용하는 것이 쉽지 않다. 영어 시소러스 기반 한국어 감정단어 추출 기술은 한국어와 영어 단어가 일대일로 일치하기가 어렵다는 문제가 있다. 기존 관련 연구 중 k-Structure 기법은 패턴의 길이가 3인 단순한 문장에 속성단어와 감정단어가 포함되었을 경우를 기준으로 한 것이므로 한정적이다. 본 논문에서 제안하는 방법은 상품평에서 의미적인 패턴을 추출하여 감정 단어의 위치를 파악하는 방법이다.

• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.414-426
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• 2014

The purpose of this study is to examine the effects of business startup attributes on the overconfidence of business startup and entrepreneur's attitude in foodservice industry. To achieve this, a survey was carried out to 300 entrepreneurs in the foodservice industry through October 1 to October 30, 2012. The results were as follows. First, entrepreneur's characteristics, funds for business startup, locations and products had a statistically significant positive effect on the overconfidence of business startup(p<.05). Second, funds for business startup, locations and products had a statistically significant positive effect on entrepreneurs' attitude(p<.01). Third, the overconfidence of business startup had a statistically significant positive effect on entrepreneurs' attitude(p<.001). The findings stated above mean that knowledge or experience related to specialized management mind as entrepreneur's characteristic factors are just as important, and customers' use and choice of restaurants are also connected to stable management when the floating population of them are located to a suitable place in combination with product characteristics in enough financing and preparation based on this, showing entrepreneur's positive attitude through stable management along with confidence.

• Korea Real Estate Review
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• v.24 no.1
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• pp.91-104
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• 2014

This paper studied condo selection attributes that affected satisfaction, recommendation and revisitation, in particular, investigated gender and age differences. Research target is the group who revisited time-sharing condominium within one year. The paper seeks to understand factors that affect and contribute to customer satisfaction and intentions for reuse. This study model was analyzed by the basic statistical analysis, factor analysis, reliability analysis and multiple analysis, using SPSS 18.0 and AMOS 18.0. We found that 5 condo selection attributes that have significant affect on user satisfaction: facility, service, product, accessibility and expense. Furthermore it was evident that user satisfaction has a significant effect on condo recommendation and intentions of reuse. With regard to sex, for male users expense, accessibility and service had a significant effect on their satisfaction level, while for female users, product was most important. User satisfaction both have a significant effect on recommendation and intentions of reuse but for females this was more evident. Regarding the age, for 20~30 age band, service and product factor had a significant effect on user satisfaction in order, whereas, for the age band of over 40s, expense, product and facility factors were important. User satisfaction of both have a significant effect on recommendation and intentions of reuse. In the meantime user satisfaction of 20~30 age band had a bigger positive significant effect on recommendation and intentions of reuse than the age band over 40s.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.81-95
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• 2023

In this study, the effect of platform selection attributes on customer satisfaction by mediating supplier trust for agrifood platform users was analyzed. The sub-variables of the selection attribute were classified into diversity, convenience, information, commerciality, and interactivity. The collected 190 questionnaires, SPSS v24.0 and Process macro 4.0 were used for empirical analysis. As a result of the analysis, first, it was found that information, commerciality, and interactivity affect supplier trust in a positive direction, and diversity and convenience do not affect supplier trust. Second, it was confirmed that supplier trust had a significant positive effect on customer satisfaction. Third, convenience, information, commerciality, and interactivity were found to affect customer satisfaction in a positive direction, and diversity did not affect customer satisfaction. It was tested to mediate between informational, commercial, interactivity and customer satisfaction. These results suggest that the platform's informativeness, commerciality, and interactivity can increase customer satisfaction by increasing supplier trust. As a follow-up study, it is recommended that the analysis of moderated mediating effects by the conditional process model including moderating variables or research to discover third mediator(s) other than the supplier trust used in the study is needed.

• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.311-324
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• 2012

This study is to investigate consumer's purchasing behavior by types of retail store and to conduct Importance-Performance Analysis(IPA) to exhort adequate competitive strategy to the traditional market. The results of this study are as follows. First, the result shows that consumer spends more time and less money at the traditional market due to the difficulties of store searching by store arrangement and construction of the market. Second, this study suggest that the traditional market needs to change their competitive strategy in same level with the discount stores, the traditional market can concentrate their capability on specialization and uniqueness of their product to meet the consumer's needs. Third, according to the result on quadrant(concentrate here) of IPA, traditional market is marked on ease of refund, convenience of after service and parking facility, and cleanliness of bathroom while the discount store and the department store are marked on high credibility level and quality of product.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.686-687
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• 2022

Existing clothing product search is possible only with text search, so it is not possible to properly search for the product that the user is looking for, and it takes a lot of time to search for the desired product. In addition, it was difficult to know where to sell unbranded products such as Soho Mall products, and it was difficult to search for detailed attributes of fashion products. To solve this problem, we would like to propose a deep learning technique that can search for the exact information that the user wants through the learned image search.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.121-123
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• 2004

본 논문은 데이터 마이닝에서 사용되는 연관규칙(Association Rule)을 활용하여 고객에게 상품을 추천하는 방법을 제안한다. 일반적으로 한명의 고객에 대하여 적용할 수 있는 연관규칙의 개수가 한 개 이상이 될 수 있다는 가정하에, 고객과 연관규칙과의 적합성 여부를 값으로 나타내는 방안을 고안하고 이를 이용하여 고객에 대한 연관규칙의 순위를 부여하는 방식을 연구한다. 또한 상품 추천 시 범위 값을 가지는 속성을 고려하여 상품을 추천하도록 하는 방법을 제안하고 이 방법의 타당성과 타 방식과의 비교우위를 실험을 통하여 검증한다.