• Title/Summary/Keyword: New E-Commerce System

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A Study of Policy Direction on O2O industry developing (O2O산업 발전을 위한 정책방향 연구)

  • Kim, Hee Yeong;Song, Seongryong
    • Journal of Digital Convergence
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    • v.15 no.5
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    • pp.13-25
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    • 2017
  • The purpose of this study is to suggest the direction of O2O industry policy for solving the conflict problems with the traditional industry stakeholder and for enhancing the regulations as new industry development is inevitable. We make use of TAIDA that is one of scenario methods to accomplish the purpose and suggest the direction of policy. First, it is needed to prepare directly by government the environment that new business models are able to emerge easily with various consulting services and information supports like public system servers and IT infra, it is practical support policy. Second, positive legal application for new business and making the law for new business are needed in legal issues situation as soon as possible. Third, the conflicts with old and new industry would be managed to the direction of "predictable" progressively. Incongruity among laws, safety and security problems, and the conflict of stakeholder are urgent. Because of the limit in this study, it is expected that O2O industry is categorized in detail aligned to the characteristics and that new policies along to the separate industry areas are developed by the following study.

Resolving the 'Gray sheep' Problem Using Social Network Analysis (SNA) in Collaborative Filtering (CF) Recommender Systems (소셜 네트워크 분석 기법을 활용한 협업필터링의 특이취향 사용자(Gray Sheep) 문제 해결)

  • Kim, Minsung;Im, Il
    • Journal of Intelligence and Information Systems
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    • v.20 no.2
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    • pp.137-148
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    • 2014
  • Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used

    . Past studies to improve CF performance typically used additional information other than users' evaluations such as demographic data. Some studies applied SNA techniques as a new similarity metric. This study is novel in that it used SNA to separate dataset. This study shows that performance of CF can be improved, without any additional information, when SNA techniques are used as proposed. This study has several theoretical and practical implications. This study empirically shows that the characteristics of dataset can affect the performance of CF recommender systems. This helps researchers understand factors affecting performance of CF. This study also opens a door for future studies in the area of applying SNA to CF to analyze characteristics of dataset. In practice, this study provides guidelines to improve performance of CF recommender systems with a simple modification.

  • Enhancing Predictive Accuracy of Collaborative Filtering Algorithms using the Network Analysis of Trust Relationship among Users (사용자 간 신뢰관계 네트워크 분석을 활용한 협업 필터링 알고리즘의 예측 정확도 개선)

    • Choi, Seulbi;Kwahk, Kee-Young;Ahn, Hyunchul
      • Journal of Intelligence and Information Systems
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      • v.22 no.3
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      • pp.113-127
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      • 2016
    • Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

    An Ontology-Driven Mapping Algorithm between Heterogeneous Product Classification Taxonomies (이질적인 쇼핑몰 환경을 위한 온톨로지 기반 상품 매핑 방법론)

    • Kim Woo-Ju;Choi Nam-Hyuk;Choi Dae-Woo
      • Journal of Intelligence and Information Systems
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      • v.12 no.2
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      • pp.33-48
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      • 2006
    • The Semantic Web and its related technologies have been opening the era of information sharing via the Web. There are, however, several huddles still to overcome in the new era, and one of the major huddles is the issue of information integration, unless a single unified and huge ontology could be built and used which could address everything in the world. Particularly in the e-business area, the problem of information integration is of a great concern for product search and comparison at various Internet shopping sites and e-marketplaces. To overcome this problem, we proposed an ontology-driven mapping algorithm between heterogeneous product classification and description frameworks. We also peformed a comparative evaluation of the proposed mapping algorithm against a well-Down ontology mapping tool, PROMPT.

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    The Construction of Multiform User Profiles Based on Transaction for Effective Recommendation and Segmentation (효과적인 추천과 세분화를 위한 트랜잭션 기반 여러 형태 사용자 프로파일의 구축)

    • Koh, Jae-Jin;An, Hyoung-Keun
      • The KIPS Transactions:PartD
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      • v.13D no.5 s.108
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      • pp.661-670
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      • 2006
    • With the development of e-Commerce and the proliferation of easily accessible information, information filtering systems such as recommender and SDI systems have become popular to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. Until now, many information filtering methods have been proposed to support filtering systems. XML is emerging as a new standard for information. Recently, filtering systems need new approaches in dealing with XML documents. So, in this paper our system suggests a method to create multiform user profiles with XML's ability to represent structure. This system consists of two parts; one is an administrator profile definition part that an administrator defines to analyze users purchase pattern before a transaction such as purchase happens directly. an other is a user profile creation part module which is applied by the defined profile. Administrator profiles are made from DTD information and it is supposed to point the specific part of a document conforming to the DTD. Proposed system builds user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile.

    A Study on the New Freight Charging Model for Parcel Service (택배서비스의 새로운 택배요금 모델에 관한 연구)

    • Song, Young-sim;Park, Hyun-Sung
      • Journal of Digital Convergence
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      • v.19 no.5
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      • pp.135-144
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      • 2021
    • In Korea, the parcel delivery service is showing a high growth rate every year thanks to the activation of e-commerce, but the courier unit price continues to drop. Due to the low cost of parcel delivery, there is a need for improvement to normalize courier rates due to deterioration in profitability for couriers, deterioration in service for consumers, and overwork and accidents for workers. In this study, a rational rate system model and a systematic approach were presented. The study method modeled the chargeable weight by reflecting the voulumatirc weight and revenue ton by the volume and weight of the cargo, and presented a new parcel freight charge model based on the cost of delivery. In addition, a rate-determining support system was developed that can be easily, conveniently and reasonably determined on-site. In the demonstration, the rate difference was determined by relying on weight rather than volume, and 63.5% for personal courier and 40% for B2C courier were found to be inadequate. This study could be used as an alternative to solving side effects and problems at the delivery site, in the urgent need for research on ways to improve delivery prices.

    Virtual Credit Card Number Payment System with Stored Hash Value for Efficient Authentication (효율적인 인증을 위한 해시 저장방식의 가상카드번호 결제 시스템)

    • Park, Chan-Ho;Kim, Gun-Woo;Park, Chang-Seop
      • Journal of the Korea Institute of Information Security & Cryptology
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      • v.25 no.1
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      • pp.5-15
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      • 2015
    • Electronic transactions have been increasing with the development of the high-speed Internet and wireless communication. However, in recent years financial corporations and mobile carriers were attacked by hackers. And large numbers of privacy information have been leaked. In particular, in the case of credit card information can be misused in the online transaction, and the damage of this given to cardholder. To prevent these problems, it has been proposed to use a virtual card number instead of the actual card number. But it has security vulnerability and requires additional security infrastructure. In this paper, we analyzed the proposed virtual card number schemes. and we propose a new virtual credit card number scheme. In the newly proposed scheme, cardholder generates a key pair (public key/private key) and pre-register public key to the issuer. then, cardholder can pay no additional security infrastructure while still efficiently satisfy the security requirements.

    Automated Classification Scheme Generation using Product Attribute Information (상품 속성정보를 이용한 분류체계 자동생성)

    • Jang, Du-Seok;Chun, Jong-Hoon
      • The KIPS Transactions:PartD
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      • v.14D no.5
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      • pp.491-500
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      • 2007
    • In order to classify and manage on-line trading goods, the product classification scheme must be maintained. In most systems for handling product information, the classification scheme is managed manually by experts, which in general incurs a lot of time and cost. Effective management of classification system becomes more important as rapid development of industry expedites diversity and convergence of goods and services. There have been many researches on developing classification scheme, and continuing in this line of research, this paper proposes a new method for automatic generation of product classification scheme. Our main idea starts from the concept that a product is a set of attributes, and we propose a novel algorithm for automatically creating hierarchical classification scheme by utilizing inclusive relationships between products. We then prove the effectiveness of proposed algorithm by conducting an experiment.

    Automatic Control System for Cultivation Environment of Crops (농작물 육성에 필요한 환경 자동제어 시스템)

    • Ahn, Woo-young;Lee, Hyun-chang
      • Journal of the Korea Institute of Information and Communication Engineering
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      • v.20 no.11
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      • pp.2167-2171
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      • 2016
    • The purpose of the cultivated crops have been changes in the aim of improving quality production. In recent years, as people's attention on health, the demand for healthy crops such as mushrooms gradually increased. In the process of mushroom factory production, regulation of environmental factors directly affects the yield and quality of mushroom. In related to the methods of mushroom cultivation, the recent technologies apply the new technology such as sensors and IT convergence services. And then cultivating mushroom is managed effectively. Farmers use plastic greenhouse cultivation mode more and more in order to reduce the impact of outdoor environment on crop cultivation, which requires farmers to adjust the greenhouse temperature at any time. But the majority of farmers still use a thermometer to measure temperature. This paper constructs an environment that can automatically adjust the temperature, so as to measuring temperature in real time, improving the efficiency of the farm work, and reducing unnecessary labor.

    Research on the Relationship Between Social Capital and Enterprise Performance in Supply Chain Environment

    • Li, Jian;Lee, Sang-Chun;Jeong, Ha-Eun
      • Journal of Korea Trade
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      • v.24 no.4
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      • pp.34-48
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      • 2020
    • Purpose - The rapid rise of e-commerce enterprises has led to the development of the logistics industry. At the same time, some enterprises are motivated by the interests to start reducing costs and inputs, which on the contrary leads to low quality of service, thus reducing customer satisfaction. In recent years, vicious competition, violent express delivery and lack of professionalism in the logistics market have led to high annual customer complaint rate, which has resulted in the company losing many loyal customers, but also unable to obtain new customers. Therefore, to pay attention to and understand the psychological needs of customers and improve the quality of logistics distribution service has become a pressing problem for Every express company. Design/methodology - By analyzing the problems existing in logistics distribution of express companies, this paper explores various factors affecting customer satisfaction and takes consumer sentiment as a mediating variable. Through questionnaires to collect relevant data, put forward hypotheses for empirical analysis, use two different software including SPSS 21.0 and AMOS 21.0 to analyze the information, draw conclusions and make recommendations. Findings - According to the above research results, the reliability, convenience, efficiency, professional can have a positive impact on customer satisfaction through the mediating effect of their sentiment, convenience and professional on consumer sentiment and satisfaction are more significant. Originality/value - This paper the establishment of distribution service indicators related to customer satisfaction and empirical analysis can not only enrich and supplement the distribution service quality indicator system studied by the former, but also provide a theoretical basis for future research.


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