• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권9호
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• pp.3043-3067
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• 2022

With the mobile traffic in the network increases exponentially, multi-access edge computing (MEC) develops rapidly. MEC servers are deployed geo-distribution, which serve many mobile terminals locally to improve users' QoE (Quality of Experience). When the cache space of a MEC server is full, how to replace the cached videos is an important problem. The problem is also called the cache replacement problem, which becomes more complex due to the dynamic video popularity and the varied video sizes. Therefore, we proposed a new cache replacement scheme based on local video popularity and video size to solve the cache replacement problem of MEC servers. First, we built a local video popularity model, which is composed of a popularity rise model and a popularity attenuation model. Furthermore, the popularity attenuation model incorporates a frequency-dependent attenuation model and a frequency-independent attenuation model. Second, we formulated a utility based on local video popularity and video size. Moreover, the weights of local video popularity and video size were quantitatively analyzed by using the information entropy. Finally, we conducted extensive simulation experiments based on the proposed scheme and some compared schemes. The simulation results showed that our proposed scheme performs better than the compared schemes in terms of hit rate, average delay, and server load under different network configurations.

• 한국프랜차이즈경영연구
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• 제11권2호
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• pp.17-22
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• 2020

Purpose: As consumers have difficulty in brand choice due to excessive information, using brand popularity as an advertising cue (e.g., Sales No. 1, Hit Product) has been getting more attention as an effective curation strategy for decreasing consumers' cognitive efforts. Accordingly, recent studies empirically demonstrate that consumers tend to prefer and choose a brand with a popularity cue and offer a useful information regarding how to use a popularity cue in marketing communication. However, extant research has mainly focused on investigating the impact of "brand popularity in a domestic market" on consumer behaviors. Thus, little is known about the effect of "brand popularity in a foreign market" on local consumers' decision-making process. Given that domestic consumers tend to purchase imported products from overseas countries, it can be meaningful information for global companies. Therefore, this research derives and tests the five hypotheses to examine how local consumers respond to brand popularity in a foreign market as an advertising cue. Specifically, it tests the three hypotheses regarding the direct and indirect effects of brand popularity in a foreign market on risk perception and purchase intention. Then, it tests two additional hypotheses about moderating effects of psychic distance on the relationship between brand popularity and risk perception as well as on the relationship between brand popularity and purchase intention. Seventy participants are exposed to an advertisement for an Indian cosmetic brand using a popularity cue in Indian market and answer the questions about brand evaluation. For data analysis, regression analysis is employed. The findings of this research show that perceived brand popularity lowers local consumers' perceived risk with a foreign brand. However, perceived brand popularity does not have a direct impact on purchase intention while it has an indirect effect through perceived risk. Meanwhile, psychic distance moderates the effect of perceived brand popularity on perceived risk level, but it has no impact on the relationship between brand popularity and purchase intention. This research is one of the first studies that demonstrate the positive impact of brand popularity in a foreign market on a local consumer's purchase decision, and it shows the effect can be moderated by psychic distance.

• 한국IT서비스학회지
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• 제16권3호
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• pp.167-183
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• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• 경영정보학연구
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• 제17권3호
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• pp.183-202
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• 2015

최근 몇 년 동안 다양한 Social Network Service(SNS)가 소비자들 사이에서 많은 인기를 누리며 새로운 커뮤니케이션 수단으로 급부상 하였다. 이에 따라 SNS를 이용한 온라인 구전 마케팅이 성행하게 되었다. 이때, 대다수의 기업들이 SNS상에서 인기(팔로워 혹은 방문자수 기준)가 많을수록 영향력이 크다는 것을 전제로 온라인 구전 마케팅의 수단으로 쓰일 SNS를 선정한다. 또한 SNS상의 인기도 혹은 영향력에 대한 기존의 연구들은 인기도와 영향력을 구분하지 않고 혼용하여 사용하고 있다. 이에 본 연구에서는 SNS상의 인기와 영향력 간의 상관관계를 실증 분석해 보고, 실제로 인기와 영향력에 영향을 미치는 변수들을 도출하고자 하였다. SNS를 사용하는 비율이 높은 20대를 대상으로 설문조사를 실시하고, PLS 2.0을 이용해 분석하였다. 연구 결과 인기와 영향력은 약한 상관관계를 가지는 것으로 나타났다. 때문에 향후 연구에서는 인기와 영향력을 구분해야 할 필요가 있으며, 영향력이 있거나 인기가 있는 SNS를 판별하기 위한 각각의 새로운 기준이 필요하다는 것을 밝혀냈다.

• 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.

• 정보처리학회논문지A
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• 제10A권3호
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• pp.261-268
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• 2003

본 논문에서는 파일이 요청된 순간에는 파일의 선호도만을 조사하고, 일정 시간이 흐른 후에 선호도가 높을 것으로 예상되는 파일들을 일관적으로 캐싱하는 새로운 캐싱 전략을 소개한다. 예측된 선호도 기반 게으른 캐싱 전략(Forecasted Popularity Based Lazy Caching Strategy)은 웹 파일의 선호도를 지수평활법을 사용하여 예측하여 기존의 캐싱 전략보다 높은 캐시 적중률을 보여준다. 국내외 5개 웹 서버로부터 수집한 로그 파일을 대상으로 실험한 결과에 의하면, 선호도의 예측이 정확할수록 높은 캐시 적중률을 나타낸다. 이는 엘 파일의 선호도 예측 기법에 대한 연구를 통해 캐시의 성능을 향상시킬 수 있음을 보여준다.

• Journal of Information Science Theory and Practice
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• 제9권2호
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• pp.18-32
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• 2021

Social media has become a popular source of information around the world. Previous studies explored different trends of social media news consumption. However, no studies have focused on Bangladesh to date, where social media penetration is very high in recent years. To fill this gap, this research aimed to understand its popularity trends during the period. For that reason, this work analyzes 97.67 million page likes and 3.48 billion interaction data collected from nine Bangladeshi news media's Facebook pages between December 2016 to November 2020. The analysis shows that the growth rates of page likes and interaction rates declined during this period. It suggests that the media's Facebook pages are gradually losing their popularity among Facebook users, which may have two more interpretations: Facebook's aggregate appeal as a news source is decreasing to users, or Bangladeshi media's appeal is eroding to Facebook users. These findings challenge the previous results, i.e., Facebook's demand as a news source is increasing with time. We offer four explanations of the decreased popularity of Facebook's news: information overload, exposure to incidental news, users' selective exposure and different aims of using Facebook, and conflict between media agendas and users' interests. Some theoretical and practical significance of the results has been discussed as well.

• Management Science and Financial Engineering
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• 제11권3호
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• pp.137-153
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• 2005

Collaborative filtering-based recommendation systems make personalized recommendations based on users' ratings on products. Recommender systems must collect sufficient rating information from users to provide relevant recommendations because less user rating information results in poorer performance of recommender systems. To learn about new users, recommendation systems must first present users with an initial item list. In this study, we designed and analyzed seven selection strategies including the popularity, favorite, clustering, genre, and entropy methods. We investigated how these strategies performed using MovieLens, a public dataset. While the favorite and popularity methods tended to produce the highest average score and greatest average number of ratings, respectively, a hybrid of both favorite and popularity methods or a hybrid of demographic, favorite, and popularity methods also performed within acceptable ranges for both rating scores and numbers of ratings.

• 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.

• Journal of the Korean Data and Information Science Society
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• 제21권3호
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• pp.605-619
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• 2010

Caching of frequently accessed data in multi-hop ad hoc environment is a technique that can improve data access performance and availability. Cooperative caching, which allows sharing and coordination of cached data among several clients, can further en-hance the potential of caching techniques. In this paper, we propose a fuzzy cooperative caching scheme in mobile ad hoc networks. The cache management of the proposed caching scheme not only uses adaptively CacheData or CachePath based on data sim-ilarity and data utility, but also uses the replacement manager based on data pro t. Also, the proposed caching scheme uses a prefetch manager. When the TTL of the cached data expires, the prefetch manager evaluates the popularity index of the data. If the popularity index is larger than a threshold, the data is prefetched. Otherwise, its space is released. The performance of the proposed scheme is evaluated analytically and is compared to that of other cooperative caching schemes.