• Proceedings of the Korea Contents Association Conference
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• 2012.05a
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• pp.277-278
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• 2012

다양한 라이프스타일에 따른 유비쿼터스 환경에서 디자인 요소와 감성공학을 결합시키는 상호작용 시스템이 요구되고 있으며 많은 연구가 진행되어 왔다. IT융합기술을 이용하여 감성 디자인을 제공하는 것은 제품 서비스 전략의 중요한 요소이다. 본 논문에서는 상황 기반의 감성 분석을 이용한 자전거 디자인 추천 방법론을 제안하였다. 제안된 방법은 자신의 감성에 부합하는 자전거 디자인을 제공함으로써 이를 얻기 위한 시간과 비용을 줄여주고, 원하는 디자인 스타일에 적용하도록 한다. 감성에 따른 자전거 디자인을 추천하기 위해 협력적 필터링을 사용하여 개인화 서비스를 제공한다. 이를 사용자 인터페이스로 구축하여 논리적 타당성과 유효성을 검증하기 위해 실험적인 적용을 시도하고자 한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.242-247
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• 2009

Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

• The Journal of the Korea Contents Association
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• v.19 no.3
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• pp.171-182
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• 2019

As online shopping malls continue to grow in popularity, various chances of consumption are provided to customers. Customers decide the purchase by exploiting information provided by shopping malls such as the reviews of actual purchasing users, the detailed information of items, and so on. It is required to provide objective and reliable information because customers have to decide on their own whether the massive information is credible. In this paper, we propose a personalized recommendation method considering an item confidence to recommend reliable items. The proposed method determines user preferences based on various behaviors for personalized recommendation. We also propose an user preference measurement that considers time weights to apply the latest propensity to consume. Finally, we predict the preference score of items that have not been used or purchased before, and we recommend items that have highest scores in terms of both the predicted preference score and the item confidence score.

• Journal of the Korea Society of Computer and Information
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• v.17 no.11
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• pp.133-140
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• 2012

In this paper, we propose a music recommendation method considering users' tags by collaborative tagging in a social music site. Since collaborative tagging allows a user to add keywords chosen by himself to web resources, it provides users' preference about the web resources concretely. In particular, emotional tags which represent human's emotion contain users' musical preference more directly than factual tags which represent facts such as musical genre and artists. Therefore, to classify the tags into the emotional tags and the factual tags and to assign weighted values to the emotional tags, a tag ontology called UniTag is developed. After preprocessing the tags, the weighted tags are used to create user profiles, and the music recommendation algorithm is executed based on the profiles. To evaluate the proposed method, a conventional playcount-based recommendation, an unweighted tag-based recommendation, and an weighted tag-based recommendation are executed. Our experimental results show that the weighted tag-based recommendation outperforms other two approaches in terms of precision.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.1-8
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• 2007

In electronic commerce, it is important for users to recommend the proper item among large item sets with saving time and effort. Therefore, if the recommendation system can be recommended the suitable item, we will gain a good satisfaction to the user. In this paper, we proposed the associative relation clustering split method in the collaborative filtering in order to perform the accuracy and the scalability. We produce the lift between associative items using the ratings data. and then split the node group that consists of the item to improve an efficiency of the associative relation cluster. This method differs the association about the items of groups. If the association of groups is filled, the reminding items combine. To estimate the performance, the suggested method is compared with the K-means and EM in the MovieLens data set.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.44-47
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• 2019

스마트 폰과 태블릿 PC를 이용하여 실시간 영상 재생 서비스(OTT: Over The Top)를 이용하는 사람들이 폭발적으로 증가하고 있다. 그에 따라 실시간 영상 재생 서비스를 즐길 수 있는 수많은 콘텐츠들이 증가하고 있다. 이에 따라 사용자는 자신의 취향에 맞는 드라마가 어떤 드라마인지 찾기가 어렵다. 따라서 본 논문에서는 사용자 스타일에 가장 적합한 미국 드라마 추천 시스템을 제안하기 위하여 선호 장르 2개, 연령대, 성별, 미국인 여부를 이용하여 유클리드 방법으로 유사도를 계산하고 협업 필터링 방법을 적용하여 드라마를 추천하는 시스템을 R을 이용하여 구현하였다.

• Electronics and Telecommunications Trends
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• v.31 no.4
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• pp.97-105
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• 2016

TV나 인터넷을 통한 비디오 콘텐츠 제공 서비스는 소비자가 콘텐츠를 선택하면 해당 콘텐츠를 재생하여 콘텐츠를 소비하는 정형화된 방식에서 벗어나 다양한 형태로 부가가치를 부여하고자 하고 있다. 이는 비디오 콘텐츠 제공업자의 수익 확대에 대한 필요성과 비디오 콘텐츠 사용자들이 적극적으로 콘텐츠를 소비하는 것에서 한 발 나아가 다양한 방식으로 콘텐츠를 제작하고자 하는 욕구가 맞물려 그 유인이 확대되고 있다. 본고에서는 비디오 콘텐츠에 부가 가치를 부여하는 대표적인 방법인 비디오 콘텐츠의 상호작용성(interactivity), 비디오 콘텐츠 추천(recommendation), 콘텐츠 관련 부가정보 제공 등을 중심으로 비디오 콘텐츠에 부가 기능 혹은 서비스를 제공하는 사례를 살펴보고자 한다.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.419-428
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• 2013

Real-time accessibility and agility are required in u-commerce under ubiquitous computing environment. Most of the existing recommendation techniques adopt the method of evaluation based on personal profile, which has been identified with difficulties in accurately analyzing the customers' level of interest and tendencies, as well as the problems of cost, consequently leaving customers unsatisfied. Researches have been conducted to improve the accuracy of information such as the level of interest and tendencies of the customers. However, the problem lies not in the preconstructed database, but in generating new and diverse profiles that are used for the evaluation of the existing data. Also it is difficult to use the unique recommendation method with hierarchy of each customer who has various characteristics in the existing recommendation techniques. Accordingly, this dissertation used the implicit method without onerous question and answer to the users based on the data from purchasing, unlike the other evaluation techniques. We applied FRAT technique which can analyze the tendency of the various personalization and the exact customer.

• The Journal of the Korea Contents Association
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• v.17 no.4
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• pp.108-118
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• 2017

This study focuses on an effectiveness of socially recommended advertising on social network sites (SNSs) and investigates the impact of three critical factors on SNS advertising effectiveness - reward type for advertising recommenders' intention, product type on advertisements, and tie strength. A $2{\times}2$ factorial design was used to test the interaction effects between the two variables, reward type and product type on advertisements, moderated by tie strength. The results indicate that when participants observe socially recommended advertising, hedonic product ads with non-monetary reward shows the most effectiveness, and reward type and product type are also effective. In the combination of reward type and product type, we have confirmed the regulating factor influencing the effectiveness of social advertising according to the tie strength between the recommenders and the consumers. Strong-tie recommenders have more influence on the effectiveness of the social advertising than weak-tie recommenders. Based on these results, theoretical and practical implications were provided to refine marketing environments on SNSs.

• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.22-30
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• 2011

In the IT convergence environment changed with not only the quality but also the material abundance, it is the most crucial factor for the strategy of personalized recommendation services to investigate the context information. In this paper, we proposed the recommendation using the service ontology based context awareness modeling. The proposed method establishes a data acquisition model based on the OSGi framework and develops a context information model based on ontology in order to perform the device environment between different kinds of systems. In addition, the context information will be extracted and classified for implementing the recommendation system used for the context information model. This study develops the ontology based context awareness model using the context information and applies it to the recommendation of the collaborative filtering. The context awareness model reflects the information that selects services according to the context using the Naive Bayes classifier and provides it to users. To evaluate the performance of the proposed method, we conducted sample T-tests so as to verify usefulness. This evaluation found that the difference of satisfaction by service was statistically meaningful, and showed high satisfaction.