• 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 Processing Systems
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• v.17 no.2
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• pp.352-368
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• 2021

Sign-in point of interest (POI) are extremely sparse in location-based social networks, hindering recommendation systems from capturing users' deep-level preferences. To solve this problem, we propose a content-aware POI recommendation algorithm based on a convolutional neural network. First, using convolutional neural networks to process comment text information, we model location POI and user latent factors. Subsequently, the objective function is constructed by fusing users' geographical information and obtaining the emotional category information. In addition, the objective function comprises matrix decomposition and maximisation of the probability objective function. Finally, we solve the objective function efficiently. The prediction rate and F1 value on the Instagram-NewYork dataset are 78.32% and 76.37%, respectively, and those on the Instagram-Chicago dataset are 85.16% and 83.29%, respectively. Comparative experiments show that the proposed method can obtain a higher precision rate than several other newer recommended methods.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.903-905
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• 2012

Recent research on Augmented Reality is Actively expand and Augmented reality feature added to the social network system (Social Network System) has become a necessity. In this paper, GPS-based Augmented Reality System for Social Network is introduced, is proposed. This system can add recent check-in friends in facebook by automatically to synchronizing the location coordinate, and it could also adding location coordinates system is represented in a real-world environment by AR, is Marker-based AR system that was Commonly used AR system is a huge cost by handheld devices in processing and storage space, the disadvantages of the marker-based AR systems can be solved by using Location-based AR applications. Therefore, the proposed GPS-based Augmented Reality System for Social Network, automatically searches for the optimal speed for Wifi and 4G network to iOS Hand AR system was desired in future.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.576-589
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• 2023

To upgrade home style recommendations and user satisfaction, this paper proposes a personalized and optimized recommendation algorithm for interior design style based on local social network, which includes data acquisition by three-dimensional (3D) model, home-style feature definition, and style association mining. Through the analysis of user behaviors, the user interest model is established accordingly. Combined with the location-based social network of association rule mining algorithm, the association analysis of the 3D model dataset of interior design style is carried out, so as to get relevant home-style recommendations. The experimental results show that the proposed algorithm can complete effective analysis of 3D interior home style with the recommendation accuracy of 82% and the recommendation time of 1.1 minutes, which indicates excellent application effect.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.762-767
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• 2013

Recently, researches on augmented reality(AR) are actively being conducted, and on addition of AR in social network system has become a necessity. In this paper, we propose GPS-based AR system for social network. This proposed system adds the recent check-in coordinates by automatically synchronizing a friend list in facebook and represents those added location coordinates in a real-world environment by using AR. Marker-based AR system that was commonly used by existing users consumes too much storage space and processing frequency for driving handle devices. But, location-based AR application can solves the disvantages of the standard marker=based AR system. Therefore, this proposed system allows an user with iOS hand devices to use GPS-based AR system by automatically searching the optimal speed for wifi and 4G. This will improve social network service.

• Journal of Communications and Networks
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• v.17 no.2
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• pp.126-132
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• 2015

In mobile computing research area, it is highly desirable to understand the characteristics of user movement so that the user friendly location aware services could be rendered effectively. Location based social networks (LBSNs) have flourished recently and are of great potential for movement behavior exploration and datadriven application design. While there have been some efforts on user check-in movement behavior in LBSNs, they lack comprehensive analysis of social influence on them. To this end, the social-spatial influence and social-temporal influence are analyzed synthetically in this paper based on the related information exposed in LBSNs. The check-in movement behaviors of users are found to be affected by their social friendships both from spatial and temporal dimensions. Furthermore, a probabilistic model of user mobile behavior is proposed, incorporating the comprehensive social influence model with extent personal preference model. The experimental results validate that our proposed model can improve prediction accuracy compared to the state-of-the-art social historical model considering temporal information (SHM+T), which mainly studies the temporal cyclic patterns and uses them to model user mobility, while being with affordable complexity.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.11
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• pp.1323-1330
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• 2014

As for civil service support, the interaction between citizens as well as the government and citizens has an important value, and the participation between citizens be very important. The interaction between these citizens can lead to spreading the use of the app by getting them to share their status information with an specified number of people in the public service such as a social network service. Mobile-based social network service has various built-in sensors to increase the interaction between the government and citizens and post the status information of users on SNS. In this regard, this paper proposed a social network application for civil services based on the Android by using location-based context information. Experimental results showed that since the application proposed in this paper can be applied to low-performance system and exhibits high performance, it can allow users located at a short distance to detect new information and share contents by adding the content to SNS which is the closest to the location of users.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.64-75
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• 2018

With the development of location-aware technologies and the activation of smart phones, location based social networks(LBSN) have been activated to allow people to easily share their location. In particular, studies on recommending the location of user interests by using the user check-in function in LBSN have been actively conducted. In this paper, we propose a location recommendation scheme considering time and user activities in LBSN. The proposed scheme considers user preference changes over time, local experts, and user interest in rare places. In other words, it uses the check-in history over time and distinguishes the user activity area to identify local experts. It also considers a rare place to give a weight to the user preferred place. It is shown through various performance evaluations that the proposed scheme outperforms the existing schemes.