• Journal of information and communication convergence engineering
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• v.18 no.1
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• pp.55-60
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• 2020

As personalized customer services create a society that emphasizes the personality of an individual, the number of product reviews and quantity of user data generated by users on the internet in mobile shopping apps and sites are increasing. Such product review data are classified as unstructured data. Unstructured data have the potential to be transformed into information that companies and users can employ, using appropriate processing and analyses. However, existing systems do not reflect the detailed information they collect, such as user characteristics, purchase preference, or purchase priority while analyzing review data. Thus, it is challenging to provide customized recommendations for various users. Therefore, in this study, we have developed a product recommendation system that takes into account the user's priority, which they select, when searching for and purchasing a product. The recommendation system then displays the results to the user by processing and analyzing their preferences. Since the user's preference is considered, the user can obtain results that are more relevant.

• The Journal of Society for e-Business Studies
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• v.4 no.3
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• pp.179-196
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• 1999

A cyber shopping mall is a place where consumers acquire the product information and make purchase decision in the cyber space. Even though there are many advantages over traditional malls, there are still several limitations to do shopping in an existing cyber mall. One of them is the absence of efficient search tool to handle various products specifications. Existing search systems usually support the "keyword search only" with limited product information. Consumers spend lots of their time and efforts in searching products and comparing them. Recently, some web sites provide the shopping mall comparison service that supports the additional search conditions such as price and maker. These services improve the situation but it is not still acceptable. In this paper, we propose an intelligent product search system based on a mediator which supports various conditional search for each product. Our system provides consumers with search results that satisfy purchase specifications. Our system is implemented in Visual basic and pert and experimental results show satisfactory performance.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.510-514
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• 2004

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering is known to be the most successful recommendation technology, but its widespread use has exposed some problems such as sparsity and scalability in the e-business environment. In this paper, we propose a recommendation methodology based on Web usage mining and product taxonomy to enhance the recommendation quality and the system performance of original CF-based recommender systems. Web usage mining populates the rating database by tracking customers' shopping behaviors on the Web, so leading to better quality recommendations. The product taxonomy is used to improve the performance of searching for nearest neighbors through dimensionality reduction of the rating database. Several experiments on real e-commerce data show that the proposed methodology provides higher quality recommendations and better performance than original collaborative filtering methodology.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.25 no.1
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• pp.35-41
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• 2002

XML(eXtensible Markup Language) based electronic catalog is very useful for searching target information because of its structural and contents based searching support capability. And XML document editing is easier than HTML because of XML document is divided by structure, contents and presentation. This paper is to present a prototype of XML based Electronic Catalog Management System(ECMS) whose system consists of data input, output and manipulation system for inserting, updating, editing and deletion. A proposed system could resolved the problems at virtual intermediary shopping mall invloved in the difficulty of interoperability when customer try to compare similar products at mixed shopping mall and reduced web service costs at independent shopping mall by using XML format. The proposed ECMS offers rapid response capability for product data change of electronic catalog and easy and friendly interoperability among similar products.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.452-457
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• 1992

Cur-rent available methods for generating assembly sequences have a large undesirable search-space. This paper presents a method for reducing the search-space. The method acquires explicitly assembly constraints caused by not only the geometry of parts but also the connectivity between the parts, in simplified form. Then the method generates assembly sequences without searching undesirable tasks using the assembly constraints. If these undesirable tasks are excluded, assembly sequences can be generated by searching only a fraction of all assembly tasks for a product and its subassemblies.

• Journal of Korea Society of Industrial Information Systems
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• v.4 no.4
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• pp.79-87
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• 1999

This paper proposes a searching system with a mobile agent which retrieve data from the distributed hosts. The proposed system dispatched the movable objects, so called AGLET, to the distributed hosts in order to perform a task given by a client or a server. The network of the existing system must not be disturbed during a task is performing. However our system has the strong point that a task can be performed even if the network is disconnected on the way. When the network is disconnected, the system can get the results later after the network system is connected again. Designing the system has been done by using UML(Unified Modeling Language) which is a standardized object-oriented modeling language. AGLET, a pure JAVA product of IBM, is used for the mobile agent.

• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.

• Journal of Information Technology Services
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• v.4 no.2
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• pp.79-97
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• 2005

With the proliferation of EC, many customers have been participating into online auction or group-buying market over searching internet shopping-malls to purchase goods. To support the customer's convenient transaction, lots of agent systems are developed to facilitate the customer's product brokering, merchant brokering, and negotiation of transaction. However, many of these systems have focused upon supporting transaction at only one type of marketplace. Since these marketplaces have different transaction settlement mechanism, it is necessary to develop the system that utilizes the customer's transaction including the selection of the type of marketplace to buy. Then we classify the kinds of B2C electronic marketplace according to the protocol by which the market operated. The structure of negotiation message is developed using XML schema and RDFS under the characteristics and message example of each marketplace in this research.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.319-322
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• 1995

As a trend toward multi-product batch processes is increasing in Chemical Process Industry (CPI), multi-product batch scheduling has been actively studied. But the optimal production scheduling problems for multi-product batch processes are known as NP-complete. Recently Ku and Karimi [5] have studied Simulated Annealing(SA) and Jung et al.[6] have developed Modified Simulated Annealing (MSA) method which was composed of two stage search algorithms for scheduling of batch processes with UIS and NIS. Jung et al.[9] also have studied the Common Intermediate Storage(CIS) policy which have accepted as a high efficient intermediate storage policy. It can be also applied to pipeless mobile intermediate storage pacilities. In spite of these above researches, there have been no contribution of scheduling of CIS policy for chemical batch processes. In this paper, we have developed another MSA for scheduling chemical batch processes with searching the suitable control parameters for CIS policy and have tested the this algorithm with randomly generated various scheduling problems. From these tests, MSA is outperformed to general SA for CIS batch process system.

• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.135-148
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• 2003

In this paper we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet Business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customer's closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.