• ETRI Journal
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• 제36권5호
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• pp.819-828
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• 2014

To solve the increasing popularity of video streaming services over the Internet, recent research activities have addressed the locality of content delivery from a network edge by introducing a storage module into a router. To employ in-network caching and persistent request routing, this paper introduces a hybrid content delivery network (CDN) system combining novel content routers in an underlay together with a traditional CDN server in an overlay. This system first selects the most suitable delivery scheme (that is, multicast or broadcast) for the content in question and then allocates an appropriate number of channels based on a consideration of the content's popularity. The proposed scheme aims to minimize traffic volume and achieve optimal delivery cost, since the most popular content is delivered through broadcast channels and the least popular through multicast channels. The performance of the adaptive scheme is clearly evaluated and compared against both the multicast and broadcast schemes in terms of the optimal in-network caching size and number of unicast channels in a content router to observe the significant impact of our proposed scheme.

• Asia pacific journal of information systems
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• 제24권2호
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• pp.191-210
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• 2014

As mobile devices that can be connected to the Internet have spread and networking has become possible whenever/wherever, the Internet has become central in the dissemination and consumption of news. Accordingly, the ways news is gathered, disseminated, and consumed have changed greatly. In the traditional news media such as magazines and newspapers, expert editors determined what events were worthy of deploying their staffs or freelancers to cover and what stories from newswires or other sources would be printed. Furthermore, they determined how these stories would be displayed in their publications in terms of page placement, space allocation, type sizes, photographs, and other graphic elements. In turn, readers-news consumers-judged the importance of news not only by its subject and content, but also through subsidiary information such as its location and how it was displayed. Their judgments reflected their acceptance of an assumption that these expert editors had the knowledge and ability not only to serve as gatekeepers in determining what news was valuable and important but also how to rank its value and importance. As such, news assembled, dispensed, and consumed in this manner can be said to be expert-based recommended news. However, in the era of Internet news, the role of expert editors as gatekeepers has been greatly diminished. Many Internet news sites offer a huge volume of news on diverse topics from many media companies, thereby eliminating in many cases the gatekeeper role of expert editors. One result has been to turn news users from passive receptacles into activists who search for news that reflects their interests or tastes. To solve the problem of an overload of information and enhance the efficiency of news users' searches, Internet news sites have introduced numerous recommendation techniques. Recommendations based on popularity constitute one of the most frequently used of these techniques. This popularity-based approach shows a list of those news items that have been read and shared by many people, based on users' behavior such as clicks, evaluations, and sharing. "most-viewed list," "most-replied list," and "real-time issue" found on news sites belong to this system. Given that collective intelligence serves as the premise of these popularity-based recommendations, popularity-based news recommendations would be considered highly important because stories that have been read and shared by many people are presumably more likely to be better than those preferred by only a few people. However, these recommendations may reflect a popularity bias because stories judged likely to be more popular have been placed where they will be most noticeable. As a result, such stories are more likely to be continuously exposed and included in popularity-based recommended news lists. Popular news stories cannot be said to be necessarily those that are most important to readers. Given that many people use popularity-based recommended news and that the popularity-based recommendation approach greatly affects patterns of news use, a review of whether popularity-based news recommendations actually reflect important news can be said to be an indispensable procedure. Therefore, in this study, popularity-based news recommendations of an Internet news portal was compared with top placements of news in printed newspapers, and news users' judgments of which stories were personally and socially important were analyzed. The study was conducted in two stages. In the first stage, content analyses were used to compare the content of the popularity-based news recommendations of an Internet news site with those of the expert-based news recommendations of printed newspapers. Five days of news stories were collected. "most-viewed list" of the Naver portal site were used as the popularity-based recommendations; the expert-based recommendations were represented by the top pieces of news from five major daily newspapers-the Chosun Ilbo, the JoongAng Ilbo, the Dong-A Daily News, the Hankyoreh Shinmun, and the Kyunghyang Shinmun. In the second stage, along with the news stories collected in the first stage, some Internet news stories and some news stories from printed newspapers that the Internet and the newspapers did not have in common were randomly extracted and used in online questionnaire surveys that asked the importance of these selected news stories. According to our analysis, only 10.81% of the popularity-based news recommendations were similar in content with the expert-based news judgments. Therefore, the content of popularity-based news recommendations appears to be quite different from the content of expert-based recommendations. The differences in importance between these two groups of news stories were analyzed, and the results indicated that whereas the two groups did not differ significantly in their recommendations of stories of personal importance, the expert-based recommendations ranked higher in social importance. This study has importance for theory in its examination of popularity-based news recommendations from the two theoretical viewpoints of collective intelligence and popularity bias and by its use of both qualitative (content analysis) and quantitative methods (questionnaires). It also sheds light on the differences in the role of media channels that fulfill an agenda-setting function and Internet news sites that treat news from the viewpoint of markets.

• 한국전자통신학회논문지
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• 제18권6호
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• pp.1095-1102
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• 2023

CCN(: Content Centric Network)은 기존 네트워크 구조를 개선하기 위해 등장한 네트워크로, 주소 대신 콘텐츠 이름에 기반하여 통신한다. 캐시를 활용하여 트래픽을 분산시키고, 중간노드에서 콘텐츠를 전송함으로써 응답시간 감소 효과를 가져오고 있다. 본 논문에서는 CCN 환경에서 제한된 CS 공간을 효율적으로 활용할 수 있도록 인기도를 고려한 캐시 정책을 제안한다. 어떤 콘텐츠에 우선순위를 두어 저장하고 방출할지를 결정하는지에 따라 CCN의 성능이 크게 달라질 수 있다. 가장 효율적인 캐시 교체를 위해 생성자 인기도, 생성자 거리, 콘텐츠 히트수를 기반으로 콘텐츠 인기도를 계산해 우선순위를 정하는 실시간 콘텐츠 인기도 기반 효율적인 캐시 교체정책을 제안하였으며, 새로운 정책의 효율성을 실험을 통해 입증하였다.

• 한국통신학회논문지
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• 제40권1호
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• pp.78-87
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• 2015

콘텐츠 중심 네트워크(CCN)는 데이터를 찾는데 필요한 라우팅 정보를 콘텐츠 이름에서 찾는 방식으로 기존의 IP 방식과는 다른 새로운 네트워킹 패러다임이다. CCN의 도전과제 중 하나인 이동성 관리는 크게 정보요청자의 이동과 정보제공자의 이동을 들 수 있다. 그 중에서도 정보제공자 이동의 경우 해당되는 라우터의 경로 정보를 갱신해야 하므로 많은 오버헤드와 시간이 필요하다. 이에 본 논문은 CCN에서 효율적인 정보제공자의 이동성 지원을 위해 콘텐츠의 인기도를 고려한 Popularity-based Pushing CCN(PoPCoN)을 제안한다. 제안하는 알고리즘은 기존의 기법과 비교하여 요청자의 콘텐츠 다운로드 시간을 단축시키고 네트워크의 오버헤드를 감소시킨다.

• 한국정보통신학회논문지
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• 제21권8호
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• pp.1498-1507
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• 2017

최근 발표에 따르면, 2020년까지 모바일 데이터 트래픽이 현재의 11배까지 증가 될 것으로 예상된다. 그 중 비디오 트래픽이 70%를 차지할 것으로 예상되는 만큼 방대해지는 모바일 비디오 트래픽의 문제를 해결하기 위해서는 비디오 트래픽의 특성을 이해할 필요가 있다. 최근, 인기 있는 유튜브 비디오와 같은 일부 인기 있는 콘텐츠의 반복적인 요청으로 인해 네트워크 트래픽 오버헤드가 많이 발생한다. 만약 콘텐츠 인기도를 알고 인기 있는 콘텐츠를 미리 캐싱 할 수 있는 네트워크 노드를 구성한다면 이용자의 요청에 대해 캐싱 된 콘텐츠를 이용함으로써 네트워크 오버헤드를 줄일 수 있다. 장치 대 장치 통신, 멀티캐스트, 헬퍼를 통해 비디오 처리량이 기존의 방법보다 약 1.5배에서 2배의 이득이 향상되었다. 또한, 프리픽스 캐싱을 통해 기존의 방법보다 약 0.2배에서 0.5배의 재생 지연이 감소되었다. 본 논문에서는 무선 네트워크 환경에서 콘텐츠 인기도를 기반한 캐싱 기술에 대한 최신 연구를 소개 한다.

• 인터넷정보학회논문지
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• 제24권6호
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• pp.49-55
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• 2023

본 연구에서는 유튜브 먹방 콘텐츠의 인기를 예측하는 모형을 제안하고 사후 분석을 통하여 먹방 콘텐츠의 인기에 영향을 주는 요인들을 식별하였다. 이를 위해 API와 Pretty Scale을 활용하여 구독자수 상위 먹방 채널들로부터 22,223개 콘텐츠의 정보를 수집하고 Random Forest, XGBoost 및 LGBM 등의 머신러닝 알고리즘을 기반으로 조회수와 좋아요수 예측모델을 구축하였다. SHAP 분석 결과 조회수 예측 모형에서는 구독자수가 예측에 가장 큰 영향을 미치는 반면, 좋아요수 예측 모형에서는 크리에이터의 매력도가 중요변수로 도출되는 등 콘텐츠 조회와 좋아요 반응에 대한 선행요인이 다름을 확인할 수 있었다. 본 연구는 대량의 온라인 콘텐츠를 분석하여 실증 분석을 진행하였다는 점에서 학술적 의의가 있으며 먹방 크리에이터들에게 시청자들의 콘텐츠 소비 경향을 알려주고 상품성 높은 콘텐츠 제작의 가이드를 제공한다는 점에서 실무적인 의의를 지닌다.

• 한국통신학회논문지
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• 제40권11호
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• pp.2151-2159
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• 2015

콘텐츠 중심 네트워크(CCN)는 기존의 IP 방식과는 다른 새로운 네트워킹 패러다임으로써 데이터를 찾는데 필요한 라우팅 정보를 콘텐츠 이름에서 찾는 방식이다. CCN은 노드마다 콘텐츠를 저장할 수 있는 캐시를 가지고 있어서 반복적인 콘텐츠 요청에 효율적으로 처리할 수 있다. 본 논문은 콘텐츠의 인기도를 활용한 캐시 교체 기법을 제안하여 기존의 기법보다 캐시의 히트율을 향상시켰고, 이에 따라 서버의 부하 및 Round Trip Time(RTT) 시간이 감소하여 성능이 향상됨을 증명하였다.

• Asia pacific journal of information systems
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• 제33권2호
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• pp.282-297
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• 2023

One of the emerging trends in the marketing field is digital video marketing. Online videos offer rich content typically containing more information than any other type of content (e.g., audible or textual content). Accordingly, previous researchers have examined factors influencing videos' popularity. However, few studies have examined what causes a video to remain popular. Some videos achieve continuous, ongoing popularity, while others fade out quickly. For practitioners, videos at the recommendation slots may serve as strong communication channels, as many potential consumers are exposed to such videos. So,this study will provide practitioners important advice regarding how to choose videos that will survive as long-lasting favorites, allowing them to advertise in a cost-effective manner. Using deep learning techniques, this study extracts text from videos and measured the videos' tones, including factual and emotional tones. Additionally, we measure the aesthetic score by analyzing the thumbnail images in the data. We then empirically show that the cognitive features of a video, such as the tone of a message and the aesthetic assessment of a thumbnail image, play an important role in determining videos' long-term popularity. We believe that this is the first study of its kind to examine new factors that aid in ensuring a video remains popular using both deep learning and econometric methodologies.

• 한국전자통신학회논문지
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• 제16권6호
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• pp.1113-1120
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• 2021

콘텐츠 중심 네트워크(CCN)은 기존 위치(IP) 기반의 네트워크 방식을 콘텐츠 이름(Content Name) 중심의 네트워크 구조로 변모시킴으로써 네트워크 전반의 효율성을 높이고자 하는 시도이다. CCN에서는 네트워크 효율을 높이기 위해 라우터 저장공간을 활용한 캐싱을 수행하는데, 캐시 교체정책은 CCN의 전반적인 성능을 좌우하는 중요한 요소이다. 따라서 CCN 분야에서는 캐시 교체정책과 관련된 많은 선행 연구가 있었다. 본 논문에서는 CCN 기본 캐시 교체정책인 LFU를 개선한 실시간 생성자 인기도 기반의 캐시 교체정책을 제안하였다. 또한, 실험을 통해 제안한 캐시 교체정책이 대조군보다 우수함을 입증하였다.

• International Journal of Knowledge Content Development & Technology
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• 제1권1호
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• pp.25-37
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• 2011

This study analyzes circulation statistics of television tie-in picture books from the Wake County Public Library System in North Carolina to determine their popularity among patrons. Caldecott winning picture books were used as a point of comparison. This study also examined OPAC holdings from North Carolina public libraries to determine television tie-in picture book popularity among collection builders. The findings of the study show that television tie-in picture books are found to some degree in the vast majority of North Carolina public libraries, and are more popular than award winners in the Wake County system.