• 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 Korea Academia-Industrial cooperation Society
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• v.15 no.7
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• pp.4237-4245
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• 2014

In this study, products in a B2B online shopping mall were classified efficiently using DEA, and an operational process is presented. The results using the data of M company were used to calculate the workload according to the category. The work load of managing the product using the DEA has been distributed evenly. In addition, the classification of A is composed of the highest net income, and it was intended to be managed centrally by the company. Business classifications C and B, which were made of a low severity workload, were reduced. Therefore, efficient operation is possible when applied to an actual business.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.1027-1038
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• 2006

There are many technical problems in the recommendation system based on very large database(VLDB). So, it is necessary to study the recommendation system' structure and the data-mining technique suitable for the large scale Internet shopping mail. Thus we design and implement the product recommendation system using k-means clustering algorithm and sequential pattern technique which can be used in large scale Internet shopping mall. This paper processes user information by batch processing, defines the various categories by hierarchical structure, and uses a sequential pattern mining technique for the search engine. For predictive modeling and experiment, we use the real data(user's interest and preference of given category) extracted from log file of the major Internet shopping mall in Korea during 30 days. And we define PRP(Predictive Recommend Precision), PRR(Predictive Recommend Recall), and PF1(Predictive Factor One-measure) for evaluation. In the result of experiments, the best recommendation time and the best learning time of our system are much as O(N) and the values of measures are very excellent.

• Journal of the Korea Computer Industry Society
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• v.2 no.7
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• pp.945-952
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• 2001

In consequence of expansion of internet users, electronic commerce is becoming a new prototype for marketing and sales, arid most of electronic commerce sites or internet shopping malls provide a rich source of information and convenient user interfaces about the organizations customers to maintain their patrons. One of the convenient interfaces for users is service to recommend products. To do this, they must exploit methods to extract and analysis specific patterns from purchasing information, behavior and market basket about customers. The methods are association rules and sequential patterns, which are widely used to extract correlation among products, and in most of on-line electronic commerce sites are executed with users information and purchased history by category-oriented. But these can't represent the diverse correlation among products and also hardly reflect users' buying patterns precisely, since the results are simple set of relations for single purchased pattern. In this paper, we propose an efficient mining technique, which allows for multiple purchased patterns that are category-independent and have relationship among items in the linked structure of single pattern items.

• Information Systems Review
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• v.24 no.4
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• pp.1-22
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• 2022

The transition to a non-face-to-face consumer society has rapidly occurred since Covid-19. The need for a subdivided urban logistics policy centered on courier delivery, a life-friendly last-mile logistics service, has been raised. This study proposes a SNS-based method that can analyze the demand relationship by region and product, respectively. We extend the market basket network (MBN) and co-purchased product network (CPN), find product category patterns, and confirm regional differences by using delivery order data. Our results imply that SNA analysis can be effectively applied to inventory distribution or product (SKU) selection strategies in urban logistics.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.107-126
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• 2021

In e-commerce, rapid and accurate automatic product classification according to product information is important. Recent developments in deep learning technology have been actively applied to automatic product classification. In order to develop a deep learning model with good performance, the quality of training data and data preprocessing suitable for the model are crucial. In this study, when categories are inferred based on text product data using a deep learning model, both effects of the data preprocessing and of the selection of training data are extensively compared and analyzed. We employ our CNN model as an example of deep learning model. In the experimental analysis, we use a real e-commerce data to ensure the verification of the study results. The empirical analysis and results shown in this study may be meaningful as a reference study for improving performance when developing a deep learning product classification model.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.109-112
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• 2006

최근 대용량 추천시스템에 대한 필요성이 증가하고 있고, 특히 대규모 인터넷 쇼핑몰을 위한 개인화 추천 시스템 구조에 대한 관심이 높아지고 있다. 본 논문에서는 k-means 클러스터링과 순차 패턴 기법을 이용한 인터넷 쇼핑몰 상품 추천 시스템을 설계 및 구현한다. 사용자 정보의 일괄처리와 카테고리의 계층적 특성을 반영하면서 데이터 마이닝 기법을 활용하여 개인화된 추천 엔진을 대형 시스템에서 동작하도록 설계 하였다. 설계 구현한 시스템의 평가를 위해, 대형 쇼핑몰의 데이터를 이용하여 추천 예측 정확율(PRP: Predictive Recommend Precision), 추천 예측 재현율(PRR: Predictive Recommend Recall), 정확도 인수(PF1 : Predictive Factor One-measure)를 구하였다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.305-312
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• 2004

인터넷 웹 사이트 상에서 사용자 행동은 클릭(click)을 단위로 모두 로그 (log)에 기록된다. 웹 서버를 통해 남는 웹로그를 가공하여 단순한 통계 수치 외에, 사용자 행동을 분석할 수가 있다. 특히 인터넷 쇼핑몰에서 사용자의 행동에 대한 분석은 중요하며, 고객의 획득, 유지 전략을 수립하기 위한 중요한 정보가 된다. 본 논문에서는 인터넷 쇼핑몰에서의 사용자 행동을 비즈니스 관점에서 분석한다. 쇼핑몰 사이트의 유입 경로 분석의 다양한 관점에 대해 논의하며, 관심 카테고리 및 상품 분석, 첫페이지 영역별 분석 등 새로운 분석 방법에 대해 소개한다. 이와 함께, 이 분석과정에서 필요한 효율적인 데이터 구조, 운영계 데이터 베이스 정보 및 이들간의 연동방안과 분석 결과의 활용 방안을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.10b
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• pp.122-124
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• 2000

이 논문에서는 보다 효과적이고 편리한 서비스를 제공할 수 잇는 전자상거래를 위한 다중 에이전트 기반의 확장된 비교 쇼핑 시스템을 제안한다. 이 시스템은 웹 크로울링(web crawling)을 통해 비교 쇼핑 시스템의 대상이 되는 웹사이트들의 페이지 추출 정보를 입수한다. 각 쇼핑 사이트에서는 정보 추출을 위한 중심이 되는 랩퍼(wraper) 기술은 먼저 정보가 있는 페이지를 가려내고, 정보가 있다고 판명되는 페이지들에서 상품 정보의 위치 즉, 반복되는 패턴(pattern)을 추출하여 필요한 상품 기술 단위 정보를 뽑아내는 학습 알고리즘이며, 각 사이트에 맞게 만들어진 랩퍼 에이전트(wrapper agent)에 대해 유효성을 검사하는 방법론을 제시한다. 또한, 학습 시 필요한 지식(knowledge)으로서의 디렉토리(directory) 구성은 미리 만들어진 표준 카테고리(category)와 용어(terminology) 존재하에 제한적이나마 새로운 디렉토리 요소에 대해 자동으로 확장할 수 있는 방법론을 제안한다.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.