• Journal of KIISE:Computing Practices and Letters
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• v.8 no.2
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• pp.206-218
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• 2002

In this paper, we design and implement a system that presents a method for selecting and providing personalized services independently without unifying the existing system platform with shopping malls joined in the hub site. This system provides a mechanism for gathering information left behind by many clients visiting Web sites for analysis of customers property, vector model for selecting personalized services, and mechanism for providing them to customers who visited in a shopping mall joined to the hub site. In a position of shopping mall site, this kind of personalization system can provide target advertisement, point marketing, and point share service etc. without changing existing shopping mall's environment through wrapper web server. Hub site customers can get personalized services from many shopping mall sites with only once registration for the hub site.

• Journal of Convergence for Information Technology
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• v.10 no.4
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• pp.1-10
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• 2020

The performance of the personalized product recommendation system for offline shopping malls is poor compared with the one using online environment information since it is difficult to obtain visitors' characteristic information. In this paper, a mobile robot platform is suggested capable of recommending personalized advertisement using customers' sex and age information provided by Face API of MS Azure Cloud service. The performance of the developed robot is verified through locomotion experiments, and the performance of API used for our robot is tested using sampled images from open Asian FAce Dataset (AFAD). The developed robot could be effective in marketing by providing personalized advertisements at offline shopping malls.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.10
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• pp.709-721
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• 2006

The traditional broadcasting services over terrestrial, satellite and cable media have been unidirectional mass media regardless of TV viewer's preferences. Recently ich media streaming has become possible via the broadb and networks. Furthermore, since bidirectional communication is possible, personalcasting such as personalized streaming services has been emerging by taking into account the user's preference on content genres, viewing times and actors/actresses etc. Accordingly, personal media becomes an important means for content provision service in addition to the traditional broadcasting service as mass media. In this paper, we introduce a user profile reasoning method for target advertisement which is considered an important application in personalcasting service. The proposed user profile reasoning method predicts an unknown TV viewer's gender and ages by analyzing TV Viewing history data. Based on the estimated user's gender and ages, a target advertisement is provided with TV Anytime metadata. A proposed target advertisement system is developed based on the user profile reasoning and the target advertisement selection method. To show the effectiveness of our proposed methods, we present a plenty of experimental results by using realistic TV viewing history data.

• The Journal of Society for e-Business Studies
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• v.16 no.3
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• pp.249-261
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• 2011

The rapid growth of e-business market makes new online companies to start and existing offline companies to join in this area. As the number of players of this market grows rapidly, the competition among them is very intense. Many companies invest huge resources to online marketing including search advertisement, email advertisement and banner advertisement. Because these traditional online marketing activities mainly focus on how to invite visitors to their web sites, ROI of these marketing activities are getting lower. Many companies are looking for a new marketing method to escape this situation. In this paper, we propose ROMS (Realtime Online Marketing System) which supports tools to improve conversion ratio of e-commerce sites, ROMS gathers behavioral data of visitors and analyzes it in realtime. ROMS supports live chats, visitor profiling, context analysis, event detection, and live marketing. With ROMS, personalized offers based on visitors' realtime context can be made for each visitor.

• Journal of Digital Convergence
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• v.12 no.4
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• pp.183-192
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• 2014

This study aims to empirically investigate how personalized and socialized mobile media impacts on users' acceptance of mobile advertising, given the consideration of users' mobile possession attachment. For this, the current study used focus group interview and surveyed mobile users. Interestingly, the findings reveal that personalized and socialized mobile characteristicssignificantly affect users' acceptance of mobile ads. That is to say, users' personalized mobile characteristics play an important role in either awareness of mobile ads or exposure to mobile ads.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.84-86
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• 1999

인터넷 쇼핑몰에서 고객의 취향에 맞게 구성된 광고 및 정보서비스 제공을 위하여, 다수 에이전트와 광고 제공 기법을 이용하여 설계한 시스템구조와 프로토타입 시스템을 소개한다. 본 시스템에서는 고객의 프로파일을 작성하고, 구매성향 데이터를 저장하는 개인화 에이전트와 사용자의 행위를 모니터링하여 로그 파일에 저장하는 모니터링 에이전트 그리고 로그파일을 분석하여 개인화 서비스에 적용할 수 있는 유용한 마케팅 정보를 추출하는 분석 에이전트, 마지막으로 추출된 마케팅 정보와 구매력을 바탕으로 광고 선정 알고리즘을 이용하여 개인화된 광고 및 정보 서비스를 제공한다. 이러한 멀티 에이전트 기반의 인터넷 쇼핑몰구조는 전자상거래에서의 맞춤 서비스를 제공함으로써 고객의 지속적인 방문을 유도하고자 한다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.189-193
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• 2006

문맥광고 또는 컨텍스트 기반 광고란 사용자들이 선택한 웹 콘텐츠 내용을 기반으로 하여 연관성 있는 광고를 자동으로 선택하여 사용자에게 제공하는 광고기법이다. 즉, 웹 사이트를 방문하는 고객을 타겟으로 하여 그들이 찾고자 하는 것과 관련된 광고를 내보냄으로써 효과적인 광고가 이루어지도록 하는 것이다. 그러나 기존의 문맥광고는 사용자가 관심을 가지는 키워드가 아닌 광고주가 선택한 키워드를 중심으로 광고 내용을 선택하기 때문에 사용자의 실제적인 관심이 반영되지 않아 광고의 효과가 떨어지는 문제점을 가지고 있다. 이러한 문제를 해결하기 위해 본 논문에서는 사용자가 웹 콘텐츠를 선택할 때 마다 사용자의 선호도를 동적으로 학습하고, 학습된 선호도를 문맥광고에 활용하는 개인화된 문맥광고를 제안한다. 실험을 위해서 제안한 방법으로 광고를 생성해서 보여주는 웹 브라우저를 구현하여 기존의 문맥광고와 개인화된 문맥광고에 대한 사용자의 평가를 비교하였다. 실험 결과 본 논문에서 제안한 개인화된 문맥광고가 ‘콘텐츠의 내용과의 연관성’, ‘사용자의 클릭여부’ 등의 항목에서 기존의 문맥광고에 비해 우수하다는 결과를 얻을 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.277-279
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• 2000

인터넷 쇼핑몰의 급증으로 각 쇼핑몰은 경쟁적으로 표적판매의 전략으로써 상품 광고나 쇼핑 정보 등을 회원들에게 e-mail로 제공해 주고 있지만 여러 쇼핑몰에 회원으로 가입되어 있는 인터넷 사용자들에게 이런 무분별하고 획일적인 광고는 오히려 번거로운 것일 수 있으며 더욱이 그 내용이 관심 밖의 것일 경우 무가치한 정보 공해에 지나지 않게 된다. 본 논문에서는 등록된 사용자의 프로파일 정보와 학습된 쇼핑패턴을 토대로 그 사용자의 관심도와 쇼핑이 필요한 시기를 예측하여 e-mail로 개인화된 광고 및 추천서비스를 제공하는 비교 쇼핑 시스템을 제안한다. 이를 위해 상품별 구매속성이 반영된 코드를 상품의 ID로 정하여 구매속성별 분류와 검색 및 갱신이 쉽고 정확하게 이루어지도록 하였고 별도의 학습 과정 없이 코드의 검색만으로 선별된 상품을 자동으로 광고와 추천하는 것이 가능하다.

• Journal of the Korea Society of Computer and Information
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• v.21 no.12
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• pp.91-96
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• 2016

In this paper, we propose the CRM service model for analyzing and managing location based data collected by Wi-Fi and BLE. As mobile devices became personalized, enterprises became interested in individual location, and location based mobile marketing started to stand on spotlight. Location based proximity marketing is developing along with contactless data transmission technology, and payment system that uses NFC, Beacon that utilizes BLE, as well as advertisement via Wi-Fi are being serviced. We suggest the model that mobile devices can be detected and identified by MAC address with the need of being connected to Wi-FI or Bluetooth interface. MAC addresses are not associated with any specific user account or mobile phone number. The idea is to be able to measure the amount of people which are present in a certain point at a specific time, allowing the study of the evolution of data analysis and offers effective information for decision-makings.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.12
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• pp.1154-1164
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• 2010

As an useful application in broadcasting services, the targeting service has been mainly studied to improve the service satisfaction and user usage in various media service environments based on user profile, preferences, and usage history. Targeting service is expanding its domain from broadcasting contents to interstitial contents and from fixed TV devices to mobile devices. Service data also include advertisement data, coupon, and information about media contents as well as simple broadcasting data. In this paper, the targeting data service is designed and implemented on articles, advertisement and broadcasting information on the basis of the user information. To adapt this to web-based media contents, information on user profile, preferences, and usage history is newly defined on the basis of the user metadata developed in TV-Anytime Forum and the user information defined in OpenSocial. The targeting data service is implemented to generate user preferences information and usage history pattern based on the similarity among user preference, contents information, and usage history. Based on performance evaluation, we prove that the proposed targeting data service is effectively applicable to web-based media contents as well as broadcasting service.