• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.281-283
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• 2021

The representative companies mentioned in the recommendation service in the domestic OTT(Over-the-top media service) market are YouTube and Netflix. YouTube, through various methods, started personalized recommendations in earnest by introducing an algorithm to machine learning that records and uses users' viewing time from 2016. Netflix categorizes users by collecting information such as the user's selected video, viewing time zone, and video viewing device, and groups people with similar viewing patterns into the same group. It records and uses the information collected from the user and the tag information attached to the video. In this paper, we propose a method to improve video media recommendation by automatically generating metadata of video media that was written by hand.

• Smart Media Journal
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• v.12 no.7
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• pp.84-92
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• 2023

This study tested the hypothesis through confirmatory factor analysis to confirm the relationship between Brand experiences (Deviant, Entertainment, and Aesthetic experiences) in Metaverse on Interest and Immersion, and to verify whether these Interests and Immersion have a significant impact on Recommendation intention. As a result of the study, it was confirmed that all Brand experience factors had a positive (+) effect on Interest and Immersion, Interest had a positive (+) effect on Immersion, and Interest and Immersion had a positive (+) effect on Recommendation intention.

• Journal of Korea Multimedia Society
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• v.7 no.4
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• pp.592-600
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• 2004

A recommendation system, which is an application area of e-CRM in e-commerce environment, provides individualized goods recommendation service that meets the demand of individual users. In general, existing recommendation systems require extensive historic user information in application domains. However, the method of recommendation based on static historic user information needs to respond flexibly to users'demand that changes rapidly and sensitively over time and in domains including a variety of users. In addition, it is difficult to recommend for new users who are not fall into any of existing domains. To overcome such limitations and provide flexible recommendation service, this study designed and implemented CPAR (Customer Preference Analysis Recommender) system that supports customer preference analysis and filter management. The filtering management capacity of the present system eases the necessity of extensive information about new users. In addition, CPAR system was implemented in XML-based wireless Internet environment for recommendation service independent from platforms and not limited by time and place.

• Journal of the Korean Society for information Management
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• v.23 no.3 s.61
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• pp.91-125
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• 2006

Recent advancements in information technology and the Internet have caused an explosive increase in the information available and the means to distribute it. However, such information overflow has made the efficient and accurate search of information a difficulty for most users. To solve this problem, an information retrieval and filtering system was developed as an important tool for users. Libraries and information centers have been in the forefront to provide customized services to satisfy the user's information needs under the changing information environment of today. The aim of this study is to propose an efficient information service for libraries and information centers to provide a personalized recommendation system to the user. The proposed method overcomes the weaknesses of existing systems, by providing a personalized hybrid recommendation method for multimedia contents that works in a large-scaled data and user environment. The system based on the proposed hybrid method uses an effective framework to combine Association Rule with Collaborative Filtering Method.

• Korea Multimedia Society
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• v.17 no.1
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• pp.28-36
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• 2013

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.214-220
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• 2021

With the rapid spread of smartphones, it has become common to listen to music everywhere, just like background music in life, so it is necessary to create a music database that can make recommendations according to individual circumstances and conditions. This paper proposes a music recommendation model through social media. Since emotions, situations, time of day, weather, etc. are included in hashtags, it is possible to build a social media-based database that reflects the opinions of various people with collective intelligence. We use web crawling to collect and categorize different hashtags from posts with music title hashtags to use real listeners' opinions about music in a database. Data from social media is used to create a music database, and music is classified in a different way from collaborative filtering, which is mainly used by existing music platforms.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.2
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• pp.164-171
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• 2006

Recently Digital multimedia broadcasting (DMB) has been available as a commercial service. The users sometimes have difficulty in finding their preferred multimedia contents and need to spend a lot of searching time finding them. They are even very likely to miss their preferred contents while searching for them. In order to solve the problem, we need a method for recommendation users preferred only minimum information. We propose an algorithm and a system for recommending users' preferred contents using preference transition probability from user's usage history. The system includes four agents: a client manager agent, a monitoring agent, a learning agent, and a recommendation agent. The client manager agent interacts and coordinates with the other modules, the monitoring agent gathers usage data for analyzing the user's preference of the contents, the learning agent cleans the gathered usage data and modeling with state transition matrix over time, and the recommendation agent recommends the user's preferred contents by analyzing the cleaned usage data. In the recommendation agent, we developed the recommendation algorithm using a user's preference transition probability for the contents. The prototype of the proposed system is designed and implemented on the WIPI(Wireless Internet Platform for Interoperability). The experimental results show that the recommendation algorithm using a user's preference transition probability can provide better performances than a conventional method.

• Smart Media Journal
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• v.12 no.9
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• pp.89-94
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• 2023

Recommender systems provide users with the most favorable items by analyzing explicit or implicit feedback of users on items. Recently, as the size of deep-learning-based models employed in recommender systems has increased, many studies have focused on reducing inference time while maintaining high recommendation accuracy. As one of them, a study on recommender systems with a knowledge distillation (KD) technique is actively conducted. By KD, a small-sized model (i.e., student) is trained through knowledge extracted from a large-sized model (i.e., teacher), and then the trained student is used as a recommendation model. Existing studies on KD for recommender systems have been mainly performed only for implicit feedback settings. Thus, in this paper, we try to investigate the performance and accuracy when applied to explicit feedback settings. To this end, we leveraged a total of five state-of-the-art KD methods and three real-world datasets for recommender systems.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.106-107
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• 2019

본 논문은 안드로이드 환경에서 사용자의 피부색으로부터 퍼스널 컬러를 추정하고, 퍼스널 컬러와 함께 조화 색상을 추천하는 것을 목표로 한다. 이를 위해 기존 영상 처리를 이용한 퍼스널 컬러 분석 방법[1]에 색채 이론을 도입하여 입력 영상으로부터 보다 정확하게 퍼스널 컬러를 추정하는 방법을 제안한다. 결과적으로, 기존 퍼스널 컬러 이론이나 방법보다 더 개인에게 최적화된 색상을 추천할 수 있었다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1278-1280
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• 2013

최근 화장품 시장이 꾸준한 양적 성장세를 보이고 있다. 그 결과 많은 소비자들이 자신에게 맞는 화장품을 찾는 데 어려움을 호소하고 있다. 화장품을 추천해주기 위한 시스템이 존재하지만 개개인의 특성을 고려하고 있지 않아 부작용을 초래할 수 있다. 따라서 본 논문에서는 개인에게 특화된 화장품 정보를 제공하는 시스템인 Beauty Manager를 제안한다.