• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3192-3212
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• 2016

With the advent and popularity of e-commerce, an increasing number of consumers prefer to order tourism products online. A recommender system can help these users contend with information overload; however, such a system is affected by the cold start problem. Online tourism destination searching is a more difficult task than others on account of its more restrictive factors. In this paper, we therefore propose a tourism destination recommender system that employs opinion-mining technology to refine user preferences and item opinion reputations. These elements are then fused into a hybrid collaborative filtering method by combining user- and item-based collaborative filtering approaches. Meanwhile, we embed an artificial interactive module in our recommender system to alleviate the cold start problem. Compared with several well-known cold start recommendation approaches, our method provides improved recommendation accuracy and quality. A series of experimental evaluations using a publicly available dataset demonstrate that the proposed recommender system outperforms existing recommender systems in addressing the cold start problem.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11c
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• pp.2371-2374
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• 2002

많은 기업들은 협업 시스템을 사용하여 제품을 보다 효율적으로 개발하기 위해서 조직과 조직 또는 기업과 기업간의 동시성 협업을 실시간으로 지원하는 시스템에 대한 연구와 개발이 진행되고 있다. 협업설계 시스템은 가상 협동 공간에서의 공동작업을 통해 지역적으로 떨어져 있는 개발자들이 직접 만나지 않고 CAD, VR, Web, 오피스문서와 같은 기업내의 다양한 데이터를 손쉽게 공유할 수 있는 시스템으로 제품의 다양화 및 신속한 출시를 가능케 한다. 본 논문에서는 실시간 협업설계 시스템 개발에 필요한 요소기술과 주요 구성원을 먼저 소개하고 CPC (Collaborative Product Commerce) 환경을 지원하는 방안과 시스템 구축사례를 소개한다.

• Proceedings of the Korea Multimedia Society Conference
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• 2000.11a
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• pp.37-40
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• 2000

전자상거래 분야에서 급속도로 증가하고 있는 정보들 중에서 사용자가 자신의 기호에 맞는 정보들만 선택하기 위하여 각 정보를 일일이 검토하기는 어려운 일이다. 이를 보완하기 위해 자동화된 정보 여과 기술이 사용되는데 대표적인 방법들로 내용 기반 여과(content-based filtering)와 협력적 여과(collaborative filtering)가 있다. 이 중 협력적 여과 기술은 높은 질(quality)을 가지는 정보들을 여과할 수 있지만 해당 정보의 속성은 고려하지 않는다는 난점을 가지는데, 본 논문에서는 이를 보완하여 정보의 대표 속성을 중심으로 선호도 예측을 수행하는 개선된 협력적 여과 방법을 제안한다. 그리고 기존의 협력적 여과 기술들과 예측의 정확성에 대하여 성능 비교 실험을 수행함으로써 제안한 방법의 타당성을 제시한다.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.211-220
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• 2004

This paper suggests a collaborative business process between the companies that each has a restricted physical branch in its own area and wants to extend globally sales and delivery service. The companies integrate their business processes for sales and delivery using a shared product taxonomy table. We also suggest a similar product finding algorithm to make the product taxonomy table that defines product relationships to exchange them between the companies. The main idea of the proposed algorithm is using a multi-attribute decision making (MADM) to find the utility values of products in a same product class of the companies. Using the values we determine what products are similar. It helps the product manager to register the similar products into a same product sub-category. The companies then allow consumer to shop and purchase the products at their own residence site and deliver them or similar products to another sites.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.323-330
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• 2004

As mobile market grows more and more fast, the mobile contents market, especially music contents for mobile phones have recorded remarkable growth. In spite of this rapid growth, mobile web users experience high levels of frustration to search the desired music. New musics are very profitable to the content providers, but the existing collaborative filtering (CF) system can't recommend them. To solve these problems, we propose an extended CF system to reflect the user's real preference by representing the characteristics of users and musics in the feature space. We represent the musics using the music contents based acoustic features in multi-dimensional feature space, and then select a neighborhood with the distance based function. Furthermore, this paper suggests a recommendation for procedure for new music by matching new music with other users' preference. The suggested procedure is explained step by step with an illustration example.

• Journal of the Korean Data and Information Science Society
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• v.18 no.3
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• pp.659-668
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• 2007

Collaborative filtering approach for recommender systems are now widely applied in e-commerce to assist customers to find their needs from many that are frequently available. this approach makes recommendations for users based on the opinions to similar users in the system. But this approach is opened to users who present their preference to items or acquire the preference information form other users, noise in the system makes significant problem for accurate recommendation. In this paper, we analyze the relationship between the standard deviation of preference ratings for each user and the estimated ratings of them. The result shows that the possibility of the pre-filtering condition which detecting the factor of bad effect on the prediction of user's preference. It is expected that using this result will reduce the possibility of bad effect on recommender systems.

• IE interfaces
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• v.18 no.4
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• pp.390-401
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• 2005

In this paper, we propose an efficient internet-based logistics brokerage concept which can overcome the weakness of the traditional off-line method to intermediate between vehicle owners and shippers for matching empty vehicles and freights. For defining a business model based on the new concept and implementing an information system, it is necessary to analyze the business process for the internet-based logistics brokerage using a modeling methodology. In this paper, we analyze the logistics brokerage process using the UN/CEFACT Modeling Methodology (UMM) being utilized as a standard modeling methodology in the area of electronic commerce. After analyzing the business process, we can expect that the UMM can be used as a useful tool for modeling the business process of electronic commerce in which the description of the collaborative work is very important.

• International Commerce and Information Review
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• v.10 no.1
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• pp.237-256
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• 2008

In recent years, most of Supply Chain Management literature has emphasized the importance of information sharing among supply chain partners, but empirical researches are few until now, especially in Korea. The purpose of this study is to develop an empirical model focusing on information exchange among partners in supply chain, and to analyze the main factors influencing the causal relationship between information exchange and performance. Based on cross-sectional questionnaire survey on Korean manufacturing firms, this study analyzed the factors influencing information exchange such as environmental factors, organizational factors, relationship factors, and information characteristics of the sample firms. Our major findings might provide many useful insights for managers in exploring the most efficient system for information exchange among partners in the context of SCM.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.93-97
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• 2002

In this paper, we propose personalized recommendation techniques based on multidimensional scaling (MDS) method for Business to Consumer Electronic Commerce. The multidimensional scaling method is traditionally used in marketing domain for analyzing customers' perceptional differences about brands and products. In this study, using purchase history data, customers in learning dataset are assigned to specific product categories, and after then using MDS a positioning map is generated to map product categories and alternative advertisements. The positioning map will be used to select personalized advertisement in real time situation. In this paper, we suggest the detail design of personalized recommendation method using MDS and compare with other approaches (random approach, collaborative filtering, and TOP3 approach)