• Proceedings of the Korea Contents Association Conference
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• 2011.05a
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• pp.5-6
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• 2011

사용자 중심의 감성공학으로 다변화 되어가는 유비쿼터스환경에서 디자인 요소와 결합시키는 상호작용이 요구되고 있으며 많은 연구가 진행되어 왔다. IT융합기술을 이용하여 감성 디자인을 제공하는 것은 제품 서비스 전략의 중요한 요소이다. 본 논문에서는 정보 필터링을 이용한 감성공학적 자동차 디자인 지원 방법론을 제안하였다. 제안된 방법은 사용자에게 자신의 감성에 부합하는 자동차 디자인을 제공함으로써 이를 얻기 위한 시간과 비용을 줄여주고, 손쉽게 원하는 디자인 스타일에 접근하도록 한다. 현실의 상황을 활용하고 정보 필터링으로 디자인을 추천함으로써 사용자에게 지능화된 개인화 서비스를 제공할 수 있다. 이를 사용자 인터페이스로 구축하여 논리적 타당성과 유효성을 검증하기 위해 실험적인 적용을 시도하고자 한다.

• The KIPS Transactions:PartD
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• v.11D no.7 s.96
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• pp.1537-1542
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• 2004

Various studies to increase customer satisfaction of a web based system are performed actively. Also in recent days an interest about the personalization that supporting a order type service on customer's viewpoint was raised. So the studies supporting the personalization is required in a web-based marketing system. In this study, we designed an intelligent recommendation system which supporting one to one web marketing using cross selling. The proposed system used an intelligent data mining method as a concurrent cross selling and a sequential cross selling. Also, In experiment on the prototype, we show a proposed system was usable in an practical system applying the mining result.

• Asia pacific journal of information systems
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• v.16 no.3
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• pp.159-176
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• 2006

Collaborative filtering is one of the popular techniques for personalized recommendation in e-commerce. In collaborative filtering, user profiles are usually managed per product category in order to reduce data sparsity. Product diversification of Internet storefronts and multiple product category sales of e-commerce portals require cross-product category usage of user profiles in order to overcome the cold start problem of collaborative filtering. In this paper, we study the feasibility of cross-product category usage of user profiles, and suggest a method to improve recommendation performance of cross-product category user profiling. First, we investigate whether user profiles on a product category can be used to recommend products in other product categories. Furthermore, a way of utilizing user profiles selectively is suggested to increase recommendation performance of cross-product category user profiling. The feasibility of cross-product category user profiling and the usefulness of the proposed method are tested with real click stream data of an Internet storefront which sells multiple product categories including books, music CDs, and DVDs. The experiment results show that user profiles on a product category can be used to recommend products in other product categories. Also, the selective usage of user profiles based on correlations between subcategories of two product categories provides better performance than the whole usage of user profiles.

• Asia pacific journal of information systems
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• v.17 no.1
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• pp.123-145
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• 2007

There are flourishing studies in the acceptance or usage of information systems literature. Most of them have taken the pro - acceptance view. Undesirably, information technologies often provoke users' reactance or resistance. This paper explores one of the negative reactions -psychological reactance. The present paper studies the effects of the users' perception of threatened freedom and personalization degree on intention to use recommendation services. High personalization can be a major motivation for users to accept recommendation systems. However recommendation services are a two-edged sword, which not only provides users the efficiency of decision making but also poses threats to free choice. When people consider that their freedom is reduced or threatened by others, they experience the motivational state to restore the freedom. This motivational state must be considered in understanding usage of information systems, especially personalized services which are designed for persuasion or compliance. This paper empirically investigates the effect of personalization and the psychological reactance on the intention to use information systems in the personalized recommendation context. Users' perception of personalization increases the usefulness of recommendation service while their perception of threat to freedom reduces the intention to use personalized recommendation service. Findings and implications are discussed.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.385-387
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• 2022

With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.351-354
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• 2021

본 연구에서는 기존 업체들이 활용하고 있는 반려동물 생체인식 기술 기반 통합 서비스 앱을 제안한다. 이 앱은 반려동물 미등록자의 미등록 사유를 바탕으로, 접근성 및 노출 빈도가 높은 스마트폰 앱으로, 등록 방식은 안면, 비문, 홍채, DNA 등록을 활용한다. 하나의 생체인식 방법을 사용하는 것이 아닌 다중 인식 방법을 제공하고, 각 인식 방법별 정확도의 비중을 달리하여 오차를 줄이고, 기존의 등록 방식 및 앱과의 차별화를 시도하고자 한다. 또한, CUPET 앱은 단순 등록에 그치지 않고, 실종 및 유기 동물 찾기, 예방접종 주기 및 반려동물 생애주기 정보 제공, 사용자들의 데이터 및 병원 연계를 통해 반려동물 유형별 병원 추천 등의 서비스를 제공하고자 한다. 본 연구에서 제안하는 CUPET 앱을 통하여, 등록 방식의 간략화로 반려동물 등록률 증가, 개인의 반려동물 인식 장치 소유 가능으로 실종 및 유기 동물에 대한 신속한 보호가 가능할 것으로 사료된다.

• Journal of Internet Computing and Services
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• v.15 no.5
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• pp.83-93
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• 2014

Providing personalized services customized to users' needs and preferences becomes highlighted as a key area of user-context computing. It is essential for context-aware technology to be developed more intelligent and meaningful services by being widely applied to a variety of sectors and domains. SDO (Standard Development Organization) such as MPEG and W3C has been actively developed to be standardized services and to improve context-awareness services. Yet current standards related to context-aware technology, such as MPEG-7, MPEG-21, MPEG-V, and emotionML, are not capable enough to support various systems and diverse services. Against this backdrop, the MPEG User Description, referred to also as MPEG-UD Standard, is to ensure interoperability among recommendation services, which take into account user's context when generating recommendations to users. In this light, we introduce standards related to the user context and propose the structure for RD-Engine and the Remote Responsive User Interface(RRUI) system in reference to MPEG-UD. This system collects unit resources matching specific condition according to the user's contexts described by MPEG-UD. In so doing, it improves adaptive user interface considering device features in real-time. By automatically generating adaptive user interfaces tailored to an individual's contexts, the proposed system aims to achieve high-quality user experience for a complex service.

• Journal of Broadcast Engineering
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• v.9 no.4 s.25
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• pp.305-321
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• 2004

With the advent of the digital broadcasting, the audiences can access a large number of TV programs and their information through the multiple channels on various media devices. The access to a large number of TV programs can support a user for many chances with which he/she can sort and select the best one of them. However, the information overload on the user inevitably requires much effort with a lot of patience for finding his/her favorite programs. Therefore, it is useful to provide the persona1ized broadcasting service which assists the user to automatically find his/her favorite programs. As the growing requirements of the TV personalization, we introduce our automatic user preference learning algorithm which 1) analyzes a user's usage history on TV program contents: 2) extracts the user's watching pattern depending on a specific time and day and shows our automatic TV program recommendation system using MPEG-7 MDS (Multimedia Description Scheme: ISO/IEC 15938-5) and 3) automatically calculates the user's preference. For our experimental results, we have used TV audiences' watching history with the ages, genders and viewing times obtained from AC Nielson Korea. From our experimental results, we observed that our proposed algorithm of the automatic user preference learning algorithm based on the Bayesian network can effectively learn the user's preferences accordingly during the course of TV watching periods.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build 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. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.10 no.1
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• pp.167-174
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• 2015

This study examines the Personalized Oriented Customized and services in smart environments. In addition to The structure of industry is currently smart environment shifting from the manufacturing industry focusing on goods production to service industry merging and combining service and marketing. The companies are placing a higher value on the customer needs to gain a competitive edge with creation of new business model. The key dilemma in mass customization and service, though, is how product customization can be realized without increasing production cost significantly. The purpose of this study is to explore new product development strategies that facilitate mass customization and service. Specifically, we propose Crowdsourcing marketing, Digital experience technology, Recommender Systems, 3D printing technology, Flexible manufacturing systems and UX based PSS(Product-Service Systems) in new product development processes as enabling strategies for mass customization and service in smart environments.