• Journal of Intelligence and Information Systems
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• v.7 no.2
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• pp.153-177
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• 2001

Nowadays, product recommendation is one of the important issues regarding both CRM and Internet shopping mall. Generally, a recommendation system tracks past actions of a group of users to make a recommendation to individual members of the group. The computer-mediated marketing and commerce have grown rapidly and thereby automatic recommendation methodologies have got great attentions. But the researches and commercial tools for product recommendation so far, still have many aspects that merit further considerations. To supplement those aspects, we devise a recommendation methodology by which we can get further recommendation effectiveness when applied to Internet shopping mall. The suggested methodology is based on web log information, product taxonomy, association rule mining, and decision tree learning. To implement this we also design and intelligent Internet shopping mall support system based on agent technology and develop it as a prototype system. We applied this methodology and the prototype system to a leading Korean Internet shopping mall and provide some experimental results. Through the experiment, we found that the suggested methodology can perform recommendation tasks both effectively and efficiently in real world problems. Its systematic validity issues are also discussed.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5S
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• pp.1599-1608
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• 2000

Automated collaborative filtering is on the verge of becoming a popular technique to reduce overloaded information as well as to solve the problems that content-based information filtering systems cannot handle. In this paper, we describe three different algorithms that perform collaborative filtering: GroupLens that is th traditional technique; Best N, the modified one; and an algorithm that uses clustering. Based on the exeprimental results using real data, the algorithm using clustering is compared with the existing representative collaborative filtering agent algorithms such as GroupLens and Best N. The experimental results indicate that the algorithms using clustering is similar to Best N and better than GroupLens for prediction accuracy. The results also demonstrate that the algorithm using clustering produces the best performance according to the standard deviation of error rate. This means that the algorithm using clustering gives the most stable and the best uniform recommendation. In addition, the algorithm using clustering reduces the time of recommendation.

• Journal of Broadcast Engineering
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• v.18 no.1
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• pp.98-105
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• 2013

Patterns for generating and consuming contents are various in these days from conventional broadcasting contents to UCC. There are many researches on developing recommendation engines based on user's profile for providing desired contents. In this paper we propose a contents recommendation system using not only user's profile but other's profiles in closed user group of the social network based on patterns for user's consuming contents. The proposed recommendation agent update user's profile using usage history and other's profiles related to the user in the closed user group.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.106-108
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• 2003

최근 들어 인터넷의 급속한 발전으로 사용자가 처리해야할 점보의 양이 급속히 늘어나게 됨으로써 사람이 혼자만의 힘으로 이 많은 정보를 처리하는 것이 하나의 고단한 작업이 되었고, 이 작업을 돕기 위한 소프트웨어 에이전트(software agent) 의 필요성이 대두되었다. 본 논문에서는 구현한 소프트웨어 에이전트가 사용자의 업무보조 (personal assistant) 라는 자신의 임무를 수행하기 위하여 온톨로지(ontology)를 기반으로 사용자의 선호도(preference) 와 의사결정 패턴을 학습하여 사용자 프로파일(user Profile) 을 작성한다. 학습한 프로파일을 바탕으로 사용자의 선호도와 일치하는 제품을 추천하는 지능형 에이전트를 제안하고. 실질적인 실험을 통해 학습된 사용자의 성향을 분석한다.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.427-432
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• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• Proceedings of the Korean Fiber Society Conference
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• 2003.10a
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• pp.112-112
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• 2003

A Web-based fabric information system has been developed. Recently the numbers of textile companies that have their own homepage to advertise their product fabrics for apparel through the Web-based E-commerce rapidly increase.(omitted)

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.109-114
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• 2002

E-commerce customers can reduce purchasing cost by the help of comparison shopping agents that collect price information of products in the shopping malls. However, user expects a software agent that can recommend product information satisfying various purchase conditions besides price. In this paper, we present a MAS (multi-agent system) which retrieves and recommends book information suitable for various user needs to realize an agent-based E-Commerce. We implemented and tested our MAS to help on-line bookstore customers. From the results, we could provide E-commerce customers various book purchase conditions for several online bookstores in real-time.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.333-338
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• 2006

최근 사용자에 대한 많은 정보를 얻는 것이 가능해지면서, 데이터마이닝 기법이나 Contents 추천 기법을 이용한 맞춤형 서비스가 가능하게 되었다. 특히, 대부분의 사람들에게 TV 프로그램 시청은 여가생활시간에서 가장 높은 비중을 차지 하고 있다. 따라서, 보다 지능적인 TV 프로그램 서비스를 제공하는 기술에 대하여 관심이 고조되고 있다. 본 논문에서는 TV-Anytime을 이용하여 개인화된 Electronic Program Guide (EPG)를 생성하고, 개인화된 EPG 정보를 활용하여 시청자에게 맞춤형 TV 프로그램 서비스를 제공하는 시스템에 대한 연구 결과를 제시한다. 또한 시청자의 시청패턴과 TV 프로그램 선호도를 바탕으로 시청자가 원하는 프로그램을 추천하는 TV Program Recommender Agent와 방송 및 TV 프로그램에 대한 대화를 담당하는 TV Program Helper Agent, 시스템 조정 및 메시지 전달을 담당하는 Coordinator Agent로 이루어진 멀티에이전트 기반 시스템 구조를 제시한다.

• Information Systems Review
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• v.8 no.1
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• pp.181-201
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• 2006

As the Internet spreads, many people have interests in e-CRM and product recommender systems, one of e-CRM applications. Among various approaches for recommendation, collaborative filtering and content-based approaches have been investigated and applied widely. Despite their popularity, traditional recommendation approaches have some limitations. They require at least one purchase transaction per user. In addition, they don't utilize much information such as demographic and specific personal profile information. This study suggests new hybrid recommendation model using two data mining techniques, association rule and classification, as well as intelligent agent to overcome these limitations. To validate the usefulness of the model, it was applied to the real case and the prototype web site was developed. We assessed the usefulness of the suggested recommendation model through online survey. The result of the survey showed that the information of the recommendation was generally useful to the survey participants.

• The Journal of the Convergence on Culture Technology
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• v.6 no.3
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• pp.267-273
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• 2020

Beacon is a wireless communication device that can automatically recognize the smart device in the short distance and transmit the necessary data, Beacon is a representative Internet of Things (IoT) facility in the era of the 4th Industrial Revolution, which is utilized in various fields such as short-distance information delivery, mobile location service, shopping, and marketing, and is constantly evolving. In this paper, it is based on tourist site-based recommendation information service. A system is proposed that recommends customized information according to the user's interest, preference, etc. by incorporating beacon technology. In other words, it acts as an information agent that informs tourists of desired information. In order to meet the needs of tourists, it is necessary to build an intelligent tourism recommendation system. The personalized Korean food and tourism recommendation management system using the beacon technology proposed in this paper is expected to provide high-quality services not only to foreigners visiting Korea but also to Korean tourists.