• 한국컴퓨터정보학회논문지
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• 제17권8호
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• pp.81-90
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• 2012

디지털 TV에서 시멘틱 환경의 유헬스 개인화 서비스 추천은 개인의 신체조건, 질병, 건강상태를 평가해서 이루어져야 한다. 기존의 시멘틱 환경의 유헬스 개인화 추천 방법은 온톨로지에 의존하여 의미 분석으로 추천을 하기 때문에 사용자 만족도가 떨어진다. 이에 본 논문에서는 디지털 TV에서 시멘틱 환경의 유헬스 서비스를 위한 나이브 베이지안 필터링 기반 개인화 서비스 추천 방법을 제안한다. 제안하는 방법은 온톨로지를 이용하여 상황데이터를 추론하여 트렌젝션을 저장 하고, 선호도 정보를 이용한 나이브 베이지안 필터링 기법을 사용하여 온톨로지로부터 생성된 트렌젝션과 사용자 선호도 정보를 이용하여 추론하여 서비스를 제공한다. 나이브 베이지안 필터링 기반으로 추론된 서비스는 기존의 필터링 방법 보다 콘텐츠 추천의 높은 정확도와 재현율을 보인다.

• 대한임베디드공학회논문지
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• 제3권4호
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• pp.182-192
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• 2008

The personalized service becomes an important role in consuming the mobile broadcasting contents due to the lack of television watching time in the mobile environment. The paper discusses the technology development of ECG (electronic content guide) for personalized broadcasting service such as contents tracking, time shifting and contents scraping according to user preference.

• 대한임베디드공학회논문지
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• 제3권4호
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• pp.198-206
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• 2008

Internet protocol television (IPTV), one of the most emerging services, offers multimedia streaming services with security, reliability, and relevant quality of service (QoS)/quality of experience (QoE). It provides added values to all the involving players and also brings technical and business challenges to them. For IPTV services, we expect to adopt the next generation network (NGN) environment for high quality and the Web technologies for personalization to meet the customer's necessity. Web can provide an open, flexible, and agile platform. Therefore, in this paper, we propose personalized IPTV services based on Web-based open platform in NGN environment and present functional architecture for Web-based personalized IPTV services. Technical issues for deploying the proposed IPTV services using Web are also provided. The objective of this paper is to analyze the critical architectural issues for developing viable and feasible networking platform models for personalized IPTV services.

• 한국정보시스템학회지:정보시스템연구
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• 제14권3호
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• pp.59-62
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• 2005

The recent broadcasting environment has shifted to embrace multimedia and multi-channel features through terrestrial, satellite and cable media. This has multiplied the number of broadcasting programs. With the information on a myriad of programs provided to TV viewers, information overload occurs, and thus, the TV viewing environment is shifting to one where viewers cannot select programs passively. Thus, a growing need is emerging to ensure a personalized electronic program guide (EPG) to help individuals select TV programs according to their taste. This paper presents a personalized EPG that enables a purpose-oriented broadcasting service to provide personalized broadcasting programs in tune with the user's requirements and preferences.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권1호
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• pp.446-465
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• 2015

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user's needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user's needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권5호
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• ETRI Journal
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• 제26권5호
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• pp.452-466
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• 2004

As the number of broadcasting channels and programs increases rapidly, the importance of personalized service has been emphasized. In this paper, we propose a practical framework of metadata broadcasting to provide personalized service according to user preferences and various terminal/network conditions. First, we present an overall system architecture of a metadata broadcasting system and then propose several core technologies (particularly in the parts of metadata authoring, metadata encoding, and metadata-based personalized content consumption). For interoperability, the proposed solution is designed to be compliant with the relevant standards of the TV-Anytime Forum, MPEG-7 Systems, and MPEG-2 Systems. Considering a home network environment, we also propose a metadata-based content adaptation scheme. Each component technology has been implemented individually, integrated into an end-to-end prototype broadcasting system, and successfully tested with a set of personalized service scenarios that are also developed in this study.

• 경영정보학연구
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• 제25권4호
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• pp.67-87
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• 2023

온라인 쇼핑 플랫폼은 개인화 추천 시스템을 활용하여 소비자의 개인 정보와 행동 데이터를 수집, 분석 및 마이닝을 통해 소비자에게 맞춤형 추천 서비스를 제공함으로써 소비자의 잠재적인 쇼핑 욕구를 자극한다. 본 연구는 S-O-R 모델을 기반으로 온라인 쇼핑 추천이 구매의도에 미치는 영양을 분석하기 위하여 시스템 품질인 다양성과 정확성, 정보 품질인 설득력과 완전성을 외부 자극으로 설정하고, 신뢰 및 지각된 가치에 따른 소비자의 심리상태 하 유기체로 설정하여 구매의도 간에 관계를 탐구하였다. 온라인 쇼핑 플랫폼을 이용하는 소비자를 대상으로 설문조사를 실시하였다. 분석결과는 개인화 추천 시스템의 품질과 정보 품질이 신뢰와 지각된 가치에 미치는 영향에 대한 가설이 모두 채택되었다. 신뢰가 시스템 품질, 정보 품질에 대한 구매의도와의 관계에서 매개역할을 확인하였으며 지각된 가치는 정보 품질에 대한 구매의도와의 관계에서 매개역할을 확인하였다. 추천 시스템이 제공하는 콘텐츠는 소비자 경험을 개선하고 소비자의 수용 정도를 높일 수 있는 방향으로 설계되어야 한다는 시사점을 도출하였다.

• 디지털융복합연구
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• 제10권2호
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• pp.287-292
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• 2012

본 논문에서는 모바일환경에서의 사용자 감정인지를 통한 개인화 서비스 지원에 필요한 위치기반 센싱 데이터의 전처리 기법과 사용자 감정 데이터의 구축 및 전처리를 위한 V-A 감정 모델에서의 감정 데이터 전처리 기법에 대하여 연구한다. 이를 위하여 그래뉼러 컨텍스트 트리 및 스트링 매칭 기반의 감정 패턴 매칭 기법을 사용한다. 또한 상황 인지를 통한 개인화 서비스를 위해 확률 기반 추론을 이용한 상황 인식 및 개인화 서비스 추천 기법에 대하여 연구한다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.276-279
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• 2004

The location-based service is one of the most powerful services in the mobile area. The location-based service provides information service for moving user's location information and information service using wire / wireless communication. In this paper, we propose a model for personalized recommendation system which includes location information and personalized recommendation system for location-based service. For this service system, we consider mobile clients that have a limited resource and low bandwidth. Because it is difficult to input the words at mobile device, we must deliberate it when we design the interface of system. We design and implement the personalized recommendation system for location-based services(advertisement, discount news, and event information) that support user's needs and location information. As a result, it can be used to design the other location-based service systems related to user's location information in mobile environment. In this case, we need to establish formal definition of moving objects and their temporal pattern.