• Journal of Korea Society of Industrial Information Systems
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• v.16 no.5
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• pp.55-62
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• 2011

Recently, LBS(Location Based Service) is expanding its service areas with the spread of smart phones and the contents of the service are more individualized according to the customer's needs. Specially, LBSNS(Location Based Social Network Service) is emerging as the most promising service of LBS. This paper introduces a LBSNS model to form a community to share common contents dynamically centering around the place of user's smart phone. The methodology suitable for implementing the effective management and the automatic update of the community of social network is presented in this paper. In addition, we describe the significant future researches of LBSNS under the upcoming mobile environment.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.6
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• pp.1-8
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• 2013

Recently, as increasing use of smartphone, the development of social network service(SNS) applications is very active because of the mobility of smartphone. In addition, as the demand of the location based service(LBS) supporting mobile information is expanded, LBS combined with SNS improves the usability of smart phones. This paper proposes the smartphone application that provides the information for SNS generated depending on the location, by tracking the user's location in real time.

• Journal of Information Technology Services
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• v.13 no.4
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• pp.205-230
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• 2014

In recent years, the social network service (SNS) and the location-based social network service (LBSNS) industry is expanding and the competition within the field is increasing much more. Since 2010, the full-scaled studies of SNS and LBSNS have begun. With the growth of SNS and LBSNS markets, SoLoMo (Social-Local-Mobile) is also becoming the trend for applications in different fields. However, despite the importance of SoLoMo, there have been little studies on the characteristics of SoLoMo applications. The purpose of this research is to investigate the effect of characteristics and perceived privacy risk of mobile location-based SNS on intention to use SoLoMo applications. For the purpose, we proposed a SoLoMo service acceptance model with TAM (Technology Acceptance Model) and the characteristics of SoLoMo applications. The characteristics consist of three factors, i.e. SNS, location, and mobile-related factors. This study also considered a gamification and a perceived privacy risk factor influencing on SoLoMo service usage in our proposed research model. The results of our empirical analysis using partial least squares (PLS) method show that the characteristics of SoLoMo applications including SNS, location, and mobile-related features, gamification, and perceived privacy risk have partially an effect on intention to use SoLoMo applications. Based on these results, SoLoMo-related companies will be able to increase the usage of SoLoMo services by differentiating their own strategies with these factors influencing on SoLoMo services.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.277-287
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• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.109-119
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• 2018

Point of interest (POI) of the city is a special place that has what importance to the user. For example, it is such landmark, restaurants, museums, hotels, and theaters. Because of its role in the social and economic life of us, these have attracted a lot of interest in location-based applications such as social networks and online map. However, while it can easily be obtained through the Web, the basic information of POI such as geographic location, another effort is required to obtain detailed information such as Wi-Fi, accepting credit cards, opening hours, romper room and the assessment and evaluation of other users. To solve these problems, a new method for correcting position error is required to link location-based social network service (LBSNS) data and POIs. This paper attempts to propose a position error correction method of POI and LBSNS data to enrich POI information from the vast information that is accumulated in LBSNS. Through this study, we can overcome the limitation of individual POI information via the information fusion method of LBSNS and POI, and we have discovered the possibility to be able to provide additional information which users need. As a result, we expect to be able to collect a variety of POI information quickly.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.960-966
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• 2012

It is prevalent to gather the location information from GPS, WiFi and etc, and therefore LBSNS (Location-Based SNS) has increased rapidly (such as location-augmented Twitter services). The message created from LBSNS include the specific area of interests which the message is created in or mentions. It is easy to propagate the location-based information of LBSNS by adapting the retweet function which is efficient way to propagate the message in tweeter. In this paper, we have defined the smart retweet as a automatic retweet function for efficient propagating the messages which is geo-tagging the location of interests. We have designed the smart retweet system based on the tweeter system. The user could specify the area of interests and build the social networking among the users which have interested in common area. The smart retweet system have been implemented by mesh-up services based on Open-API of trweeter and google map. It is expected that the smart retweet service proposed in this paper makes easy sharing of the location-based interesting information.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.4
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• pp.752-758
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• 2012

SNS(Social Networking Services) is oneline service that enable users to construct human network through their relation on web, such as following relation, friend relation, and etc. Recently, owing to the advent of digital devices (smart phone, tablet PC) which embedded GPS some applications which provide services with spatial relevance and social relevance have been released. Such an online service is called LBSNS. It is required to use spatial filtering so as to build the LBSNS system that enable users to subscribe information of interesting area. For spatial filtering, user and tweet attaches location information which divide into static property presenting fixed area and dynamic property presenting user's area changed along the moving user. In the case of using a location information including dynamic property, Continuous query occurred from the moving user causes the problem in server. In this paper, we propose spatial filtering algorithm using Virtual Grid for reducing frequency of query, and conclude that frequency of query on using Virtual Grid is 93% decreased than frequency of query on not using Virtual Grid.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.377-379
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• 2022

POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.

• Proceedings of the Korea Contents Association Conference
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• 2013.05a
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• pp.111-112
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• 2013

19대 총선에서 특정 후보를 홍보하기 위한 목적으로 개인 맞춤형 위치기반소셜네트워크서비스(LBSNS, Location Based Social Network Service) 앱(App)을 기획하여 개발하고 선거기간 동안 활용함으로써 선거 전후 해당 후보의 지지율 변화에 어느 정도 기여했는지 그 영향을 분석하였다. 대전광역시 6개 지역구 24명의 후보를 대상으로 개인 맞춤형 LBSNS 앱을 활용한 후보와 그렇지 않은 후보를 구분하여 선거운동 기간 동안 언론에 공개적으로 발표된 지지율과 개표 결과를 바탕으로 개인별 지지율 변화를 비교하였다. 해당 앱을 활용한 3명의 후보는 각각 12.6%, 11.4% 및 11.2%씩 두 자리 수의 지지율 상승이 있었지만 나머지 21명의 후보들은 지지율 변화는 모두 3% 이내로 머물러 개인맞춤형 스마트폰 앱을 활용함으로써 후보 지지율 상승에 상당한 효과가 있었음이 밝혀졌다.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.21-30
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• 2014

Lately, new influentials have secured a large number of volunteers on social networks due to vitalization of various social media. There has been considerable research on these influential people in social networks but the research has limitations on location information of Location Based Social Network Service(LBSNS). Therefore, the purpose of this study is to propose a spatial detection methodology and application plan for influentials who make comments about diverse social and cultural issues in LBSNS using spatial statistical analysis methods. Twitter was used to collect analysis object data and 168,040 Twitter messages were collected in Seoul over a month-long period. In addition, 'politics,' 'economy,' and 'IT' were set as categories and hot issue keywords as given categories. Therefore, it was possible to come up with an exposure index for searching influentials in respect to hot issue keywords, and exposure index by administrative units of Seoul was calculated through a spatial joint operation. Moreover, an influential index that considers the spatial dependence of the exposure index was drawn to extract information on the influential areas at the top 5% of the influential index and analyze the spatial distribution characteristics and spatial correlation. The experimental results demonstrated that spatial correlation coefficient was relatively high at more than 0.3 in same categories, and correlation coefficient between politics category and economy category was also more than 0.3. On the other hand, correlation coefficient between politics category and IT category was very low at 0.18, and between economy category and IT category was also very weak at 0.15. This study has a significance for materialization of influentials from spatial information perspective, and can be usefully utilized in the field of gCRM in the future.