• Title/Summary/Keyword: 모바일 소셜 네트워크

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The Study of the Effects of the Enterprise Mobile Social Network Service on User Satisfaction and the Continuous Use Intention (기업 모바일 소셜네트워크서비스 특성요인이 사용자 만족과 지속적 사용의도에 미치는 영향에 관한 연구)

  • Kim, Joon-Hee;Ha, Kyu-Soo
    • Journal of Digital Convergence
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    • v.10 no.8
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    • pp.135-148
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    • 2012
  • This work is intended to investigate how the factors of enterprise mobile SNS affect user satisfaction and continuous use intention through technology acceptance model proposed by Davis. To achieve the purpose, this researcher explored Information Systems Success model proposed by DeLone & McLean, Technology Acceptance Model proposed by Davis, and Model after Acceptance, and on the basis of the investigation, performed a study. For the data of this work, 9 enterprises, each of which has more than 100 employees and is located in Seoul, were chosen, and a questionnaire survey was conducted on their 276 employees who experienced enterprise mobile SNS. As a data collection tool, a structured self-administered questionnaire was used. For data analysis, SPSS 18.0 and AMOS 18.0 were used for applying Structural Equation modelling. According to the results of this work, three factors of enterprise mobile SNS-systematic factor (system quality, information quality, and service quality), user factor (personal innovation and personal familiarity), social factor (social effects and social interaction)-affected user satisfaction and continuous use intention through perceived availability, perceived easiness, and perceived enjoyment. Also, it was found that the direction of effects matched a theoretical prediction. And, it was revealed that the decision variables and mediating variables significantly affected user satisfaction and continuous use intention. Theoretical and practical meanings were discussed for the study result, and some suggestions were made for the issues of this work and future studies.

A study on user experience of Instagram IGTV -Focus on fashion·beauty contents service (인스타그램 IGTV의 사용자 경험 연구 -패션·뷰티 콘텐츠 서비스를 중심으로-)

  • Woo, Soo-Hee;Kim, Seung-In
    • Journal of Digital Convergence
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    • v.17 no.3
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    • pp.405-411
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    • 2019
  • The purpose of the study is to investigate a usability of using fashion and beauty service and suggest better user experience on Instagram's newly released mobile video platform, IGTV. The study expects to be a resource of improving the usability on fashion and beauty contents on IGTV and encourage further research for suggesting better guidelines. As a research method, it will experiment current mobile video service first with literature review. Afterwards, the research conducted tasks and in-depth interview with eight Instagram users to evaluate a usability of using fashion and beauty service on IGTV. As a result, it is able to derive two plans that needed improvement. Firstly, IGTV is required to have high accessibility for user's to use service longer and intuitive user experience. Secondly, unlike previous service that Instagram have offered, IGTV need to differentiate to share and get information of fashion and beauty trends.

Clustering Method based on Genre Interest for Cold-Start Problem in Movie Recommendation (영화 추천 시스템의 초기 사용자 문제를 위한 장르 선호 기반의 클러스터링 기법)

  • You, Tithrottanak;Rosli, Ahmad Nurzid;Ha, Inay;Jo, Geun-Sik
    • Journal of Intelligence and Information Systems
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    • v.19 no.1
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    • pp.57-77
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    • 2013
  • Social media has become one of the most popular media in web and mobile application. In 2011, social networks and blogs are still the top destination of online users, according to a study from Nielsen Company. In their studies, nearly 4 in 5active users visit social network and blog. Social Networks and Blogs sites rule Americans' Internet time, accounting to 23 percent of time spent online. Facebook is the main social network that the U.S internet users spend time more than the other social network services such as Yahoo, Google, AOL Media Network, Twitter, Linked In and so on. In recent trend, most of the companies promote their products in the Facebook by creating the "Facebook Page" that refers to specific product. The "Like" option allows user to subscribed and received updates their interested on from the page. The film makers which produce a lot of films around the world also take part to market and promote their films by exploiting the advantages of using the "Facebook Page". In addition, a great number of streaming service providers allows users to subscribe their service to watch and enjoy movies and TV program. They can instantly watch movies and TV program over the internet to PCs, Macs and TVs. Netflix alone as the world's leading subscription service have more than 30 million streaming members in the United States, Latin America, the United Kingdom and the Nordics. As the matter of facts, a million of movies and TV program with different of genres are offered to the subscriber. In contrast, users need spend a lot time to find the right movies which are related to their interest genre. Recent years there are many researchers who have been propose a method to improve prediction the rating or preference that would give the most related items such as books, music or movies to the garget user or the group of users that have the same interest in the particular items. One of the most popular methods to build recommendation system is traditional Collaborative Filtering (CF). The method compute the similarity of the target user and other users, which then are cluster in the same interest on items according which items that users have been rated. The method then predicts other items from the same group of users to recommend to a group of users. Moreover, There are many items that need to study for suggesting to users such as books, music, movies, news, videos and so on. However, in this paper we only focus on movie as item to recommend to users. In addition, there are many challenges for CF task. Firstly, the "sparsity problem"; it occurs when user information preference is not enough. The recommendation accuracies result is lower compared to the neighbor who composed with a large amount of ratings. The second problem is "cold-start problem"; it occurs whenever new users or items are added into the system, which each has norating or a few rating. For instance, no personalized predictions can be made for a new user without any ratings on the record. In this research we propose a clustering method according to the users' genre interest extracted from social network service (SNS) and user's movies rating information system to solve the "cold-start problem." Our proposed method will clusters the target user together with the other users by combining the user genre interest and the rating information. It is important to realize a huge amount of interesting and useful user's information from Facebook Graph, we can extract information from the "Facebook Page" which "Like" by them. Moreover, we use the Internet Movie Database(IMDb) as the main dataset. The IMDbis online databases that consist of a large amount of information related to movies, TV programs and including actors. This dataset not only used to provide movie information in our Movie Rating Systems, but also as resources to provide movie genre information which extracted from the "Facebook Page". Formerly, the user must login with their Facebook account to login to the Movie Rating System, at the same time our system will collect the genre interest from the "Facebook Page". We conduct many experiments with other methods to see how our method performs and we also compare to the other methods. First, we compared our proposed method in the case of the normal recommendation to see how our system improves the recommendation result. Then we experiment method in case of cold-start problem. Our experiment show that our method is outperform than the other methods. In these two cases of our experimentation, we see that our proposed method produces better result in case both cases.

Item Recommendation Technique Using Spark (Spark를 이용한 항목 추천 기법에 관한 연구)

  • Yun, So-Young;Youn, Sung-Dae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.5
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    • pp.715-721
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    • 2018
  • With the spread of mobile devices, the users of social network services or e-commerce sites have increased dramatically, and the amount of data produced by the users has increased exponentially. E-commerce companies have faced a task regarding how to extract useful information from a vast amount of data produced by the users. To solve this problem, there are various studies applying big data processing technique. In this paper, we propose a collaborative filtering method that applies the tag weight in the Apache Spark platform. In order to elevate the accuracy of recommendation, the proposed method refines the tag data in the preprocessing process and categorizes the items and then applies the information of periods and tag weight to the estimate rating of the items. After generating RDD, we calculate item similarity and prediction values and recommend items to users. The experiment result indicated that the proposed method process large amounts of data quickly and improve the appropriateness of recommendation better.

The Characteristics and Future Trends of Short-Form Animation (숏폼 애니메이션의 특성과 발전방향에 관한 연구)

  • Lee, Sun-Ju;Han, Je-Sung
    • Cartoon and Animation Studies
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    • s.38
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    • pp.29-51
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    • 2015
  • With the progress in high speed internet networks, mobile devices and social networking, the eco-system of the media has shifted from that where the flow of content was one-way from the producer to the consumer. A so-called 'prosumer' culture has taken root where the consumer himself produces media content. Along with these trends, various video-sharing platforms such as youtube has a method of allocating advertisement profit to the content producer, offering a win-win platform for content pro-sumers. This allows the channels to attract several tens of millions of subscribers and raise an annual income of over 10 billion Won, marking a revolutionary change in the content industry. This paper seeks to analyze video distribution channels and short-form media content that are showing continuous growth to identify new markets where animated content can make progress in an era of online video media platforms, as well as provide a future direction for small teams of creators of animated films to survive and thrive in this environment.

Design and Implementation of Personal Information Identification and Masking System Based on Image Recognition (이미지 인식 기반 향상된 개인정보 식별 및 마스킹 시스템 설계 및 구현)

  • Park, Seok-Cheon
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.17 no.5
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    • pp.1-8
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    • 2017
  • Recently, with the development of ICT technology such as cloud and mobile, image utilization through social networks is increasing rapidly. These images contain personal information, and personal information leakage accidents may occur. As a result, studies are underway to recognize and mask personal information in images. However, optical character recognition, which recognizes personal information in images, varies greatly depending on brightness, contrast, and distortion, and Korean recognition is insufficient. Therefore, in this paper, we design and implement a personal information identification and masking system based on image recognition through deep learning application using CNN algorithm based on optical character recognition method. Also, the proposed system and optical character recognition compares and evaluates the recognition rate of personal information on the same image and measures the face recognition rate of the proposed system. Test results show that the recognition rate of personal information in the proposed system is 32.7% higher than that of optical character recognition and the face recognition rate is 86.6%.

A Study on WT-Algorithm for Effective Reduction of Association Rules (효율적인 연관규칙 감축을 위한 WT-알고리즘에 관한 연구)

  • Park, Jin-Hee;Pi, Su-Young
    • Journal of Korea Society of Industrial Information Systems
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    • v.20 no.5
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    • pp.61-69
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    • 2015
  • We are in overload status of information not just in a flood of information due to the data pouring from various kinds of mobile devices, online and Social Network Service(SNS) every day. While there are many existing information already created, lots of new information has been created from moment to moment. Linkage analysis has the shortcoming in that it is difficult to find the information we want since the number of rules increases geometrically as the number of item increases with the method of finding out frequent item set where the frequency of item is bigger than minimum support in this information. In this regard, this thesis proposes WT-algorithm that represents the transaction data set as Boolean variable item and grants weight to each item by making algorithm with Quine-McKluskey used to simplify the logical function. The proposed algorithm can improve efficiency of data mining by reducing the unnecessary rules due to the advantage of simplification regardless of number of items.

Factors Influencing Information Privacy Behavior: A Replication Study

  • Kim, Gimun;Yoon, Jongsoo
    • Journal of the Korea Society of Computer and Information
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    • v.26 no.4
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    • pp.231-237
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    • 2021
  • Over a decade ago, Krasnova et al. identified the factors that influence Facebook users' self-disclosure. These factors include perceived risks, relationship building, relationship maintenance, self-presentation, and enjoyment. Meanwhile, during the past 10 years, there have been significant changes in terms of function, media, and competition. SNSs have been functionally enhanced, used in mobile environment, and had many competitors. Based on these facts, it is believed that the influence of the factors on self-disclosure is different from those of Krasnova et al. The purpose of this study is to verify through a replication study whether the factors adopted in the study of Krasnova et al. are still important in explaining self-exposure. The study empirically find the result significantly different from those of Krasnova et al. Based on the result, the study provides meaningful implications and suggestions for future research.

A study on the detection of fake news - The Comparison of detection performance according to the use of social engagement networks (그래프 임베딩을 활용한 코로나19 가짜뉴스 탐지 연구 - 사회적 참여 네트워크의 이용 여부에 따른 탐지 성능 비교)

  • Jeong, Iitae;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.197-216
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    • 2022
  • With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

A Study on How Social Comparison Between Players on Mobile Puzzle SNG When Competeing on leaderboard, Affect the Competition and Chllenge - Focused on Self-Evaluation maintenance model - (모바일 퍼즐 SNG 순위경쟁상황에서 플레이어의 사회비교가 경쟁심과 도전감에 미치는 영향 - 자기평가유지모형을 중심으로 -)

  • Kim, Jaehyun;Choi, Chris Seoyun;Kim, Hyunsuk
    • Journal of the HCI Society of Korea
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    • v.13 no.3
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    • pp.5-15
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    • 2018
  • The biggest characteristic of Social Network Game(SNG) is that games are played through competition and cooperation with the actual acquaintances based on SNS. Even though such competition and challenge spirit have been dealt importantly as preceding factors having influence on the flow in games in the existing game area, it is rare to find researches deeply considering the characteristics of ranking competition between acquaintances in SNG. Moreover, it was not considered that such acquaintances could be the targets of competition and also challenge at the same time in SNG. Therefore, this study examined the achievements(big differences in ranking, small differences in ranking) of the targets for comparison and closeness(strong ties, weak ties) with the targets for comparison as factors having influence on competition and challenge spirit, and also empirically analyzed the influence of such factors and interactions between factors on players' competition and challenge spirit in the ranking competitive society, by analyzing the characteristics of ranking competition between acquaintances in the mobile puzzle, SNG based on SNS through the analysis on the preceding research on the self-evaluation maintenance model of the social comparison theory. In the results, when preferentially exposing competitors with small difference in ranking and also exposing competitors with stronger ties, players' competition is stimulated, so that it can improve their challenge spirit. Such results of this study can be expected to a lot contribute to the actual design work of SNG ranking table contents.

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