• Proceedings of the Korea Information Processing Society Conference
• /
• 2012.04a
• /
• pp.920-922
• /
• 2012

현재 대부분의 소셜 네트워크 서비스는 폐쇄형 구조로써 서로 상호 연동이 불가능한 상태이고, 연동을 하기 위해서는 사용자의 인증을 통하여 접속을 하여야만 가능하다. 따라서 이러한 상호 연동 문제를 해결하기 위하여 크로스 소셜 미디어 플랫폼 기능은 이종 SNS 간의 연동을 통하여 각 SNS을 사일로로 편성 하고 이들간의 연동을 통해 서비스를 제공하기 위한 플랫폼으로 AR 기반 브라우져를 통하여 Open 소셜 미디어 서비스를 제공한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.698-701
• /
• 2013

Mobile social networking applications are getting increasingly popular among today's mobile applications. Mobile users find their old or new friends anywhere or anytime through mobile social network (MSN) services. MSN uses matchmaking mechanisms to discover mutual interests among different people. To discover friends in MSN, a user must share his/her private information which can be a risk for his/her personal privacy as this information can be learned by a malicious or semi honest user. In this paper we give a brief survey on MSN that includes MSN categories, components, architecture and applications. In the rest of the paper we discuss the matchmaking protocols. Finally we give some suggestions to improve the previous protocols.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.04a
• /
• pp.352-355
• /
• 2009

본 논문에서는 Mobbing(집단 따돌림) 현상에 관련된 7개의 요소(Factor)와 그 하위에 포함된 60개의 속성(Attribute)들을 선정한다. 다음으로 선정한 속성들에 대해 나와 사용자들 사이에 관계가 있으면 '1', 관계가 없으면 '0'으로 표현하고, 나와 사용자들간의 유사도 산정을 위해 각 요소안에 포함된 속성들의 합에 유사도 함수를 적용한다. 다음으로 클레멘타인의 인공신경망 알고리즘을 통해 속성들을 포함한 요소들이 취할 최적의 가중치를 산출하고, 이 값들의 총합으로 Mobbing 지수를 산정한다. 마지막으로 Social Network 사용자들의 Mobbing 지수를 본 논문에서 설계한 G2 Mobbing 성향 분류 모델(4개의 그룹; Ideal Group of the Social Network, Bullies, Aggressive victimes, Victimes)에 매핑하여 사용자들의 Mobbing 성향을 알아본다.

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

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.2
• /
• pp.744-768
• /
• 2016

As various pieces of information can be provided through the web, schemes that provide search results optimized for individual users are required in consideration of user preference. Since the existing social search schemes use users' profiles, the accuracy of the search deteriorates. They also decrease the reliability of a search result because they do not consider a search time. Therefore, a new social search scheme that considers temporal information as well as popularities and user preferences is required. In this paper, we propose a new mobile social search scheme considering popularities and user preferences based on temporal information. Popularity is calculated by collecting the visiting records of users, while user preference is generated by the actual visiting information among the search results. In order to extract meaningful information from the search target objects that have multiple attributes, a skyline processing method is used, and rank is given to the search results by combining the user preference and the popularity with the skyline processing result. To show the superiority of the proposed scheme, we conduct performance evaluations of the existing scheme and the proposed scheme.

• The Journal of the Korea Contents Association
• /
• v.17 no.8
• /
• pp.619-630
• /
• 2017

With the growing popularity of smart devices, various location based services have been providing to users. Recently, some location based social applications that combine social services and location based services have been emerged. The demands of a k-nearest neighbors(k-NN) query which finds k closest locations from a user location are increased in the location based social network services. In this paper, we propose an approximate k-NN query processing method for fast response time in a large number of users environments. The proposed method performs efficient stream processing using big data distributed processing technologies. In this paper, we also propose a modified grid index method for indexing a large amount of location data. The proposed query processing method first retrieves the related cells by considering a user movement. By doing so, it can make an approximate k results set. In order to show the superiority of the proposed method, we conduct various performance evaluations with the existing method.

• Language and Information
• /
• v.20 no.1
• /
• pp.25-49
• /
• 2016

Honorific forms express the speaker's social attitude to others and also indicate the social ranks and level of intimacy of the participants in the discourse. In a cross-linguistic perspective of grammar engineering, modelling honorification has been regarded as a key strategy for improving language processing applications. Using the HPSG and MRS formalism, this article provides a multilingual grammar model of honorification. The present study incorporates the honorific information into the Meaning Representation System (MRS) via Individual Constraints (ICONS), and then conducts an evaluation to see if the model contributes to semantics-based language processing.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.267-269
• /
• 2019

There are many recommendation systems offer an effort to get better preciseness the information to the users. In order to further improve more accuracy, the social network analysis method which is used to analyze data to community detection in social networks was introduced in the recommendation system and the result shows this method is improving more accuracy. In this paper, we propose a movie recommendation system using social network analysis and normalized discounted cumulative gain with the best accuracy. To estimate the performance, the collaborative filtering using the k nearest neighbor method, the social network analysis with collaborative filtering method and the proposed method are used to evaluate the MovieLens data. The performance outputs show that the proposed method get better the accuracy of the movie recommendation system than any other methods used in this experiment.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.38B no.7
• /
• pp.501-511
• /
• 2013

Recently, the visualization method of social network service have been only focusing on presentation of visualizing network data, which the methods do not consider an efficient processing speed and computational complexity for increasing at the ratio of arithmetical of a big data regarding social networks. This paper proposes a cloud based on visualization method to visualize a user focused hierarchy relationship between user's nodes on social network. The proposed method can intuitionally understand the user's social relationship since the method uses fuzzy to represent a hierarchical relationship of user nodes of social network. It also can easily identify a key role relationship of users on social network. In addition, the method uses hadoop and hive based on cloud for distributed parallel processing of visualization algorithm, which it can expedite the big data of social network.

• Journal of Information Technology Services
• /
• v.19 no.5
• /
• pp.65-81
• /
• 2020

This study aims to examine the effects of users' perceptions of COVID-19 risk on their seeking and processing of relevant information as COVID-19 emerges and spreads worldwide in 2019. We apply the risk information seeking and processing model (RISP Model) to verify whether users' COVID-19 related information seeking and processing behaviors have a positive effect on their preventive and information sharing behaviors. To achieve this research goal, an online survey was conducted with about 400 of social media users. The users' perceptions of risk for COVID-19 increased their perceived insufficiency of COVID-19 information. In addition, the perceived insufficiency of users' information formed a positive relationship with seeking and searching of information behaviors. The processing of COVID-19 related information has increased related preventive behaviors and sharing of information through social media. While searching for information related to COVID-19 prompted personal information sharing behaviors, it did not significantly affect preventive behaviors. Accordingly, in order to promote COVID-19 preventive behaviors as well as overall user health-related behaviors it can be inferred that additional measures are needed in addition to pursuing relevant information.