• Journal of Korea Society of Digital Industry and Information Management
• /
• v.9 no.1
• /
• pp.43-52
• /
• 2013

Pervasive computing and social network are good resources in recommendation method. Collaborative filtering is one of the most popular recommendation methods, but it has some limitations such as rating sparsity. Moreover, it does not consider social network in pervasive computing environment. We propose an effective proactive friend recommendation method using social network and contexts in pervasive computing environment. In collaborative filtering method, users need to rate sufficient number of items. However, many users don't rate items sufficiently, because the rating information must be manually input into system. We solve the rating sparsity problem in the collaboration filtering method by using contexts. Our method considers both a static and a dynamic friendship using contexts and social network. It makes more effective recommendation. This paper describes a new friend recommendation method and then presents a music friend scenario. Our work will help e-commerce recommendation system using collaborative filtering and friend recommendation applications in social network services.

• The Journal of Society for e-Business Studies
• /
• v.17 no.3
• /
• pp.15-41
• /
• 2012

Recently, there happens many purchasing cases encouraged by friends' recommendation in SNS (Social Network Service). This study investigates the effect of friend recommendation on consumers' purchasing heuristic. For this purpose, we compare the effect of friend recommendation with consumer recommendation in terms of trustworthy, specialty, relevancy. Usually, the frequency of friend recommendation is far lower than that of consumer recommendation. Hence, we examine how the effect of information source (friend recommendation or consumer recommendation) is moderated by the frequency of recommendation, as well. As results, this study finds out that, under the same frequency, friend recommendation does not have significantly stronger effect on the purchasing heuristic, although friend recommendation is evidenced as one of significant heuristic inducers. However, in terms of trustworthy, friend recommendation is significantly superior to the consumer recommendation. Moreover, under sufficiently higher frequency, friend recommendation works as better heuristic factor than consumer recommendation. The results deliver managerial implications in the perspective of understanding consumers' purchasing decisions and responding strategies of firms.

• International Journal of Contents
• /
• v.12 no.2
• /
• pp.37-41
• /
• 2016

In this paper, we propose a friend recommendation scheme that takes into consideration the attribute information of a POI and a user's movement patterns. The proposed scheme broadly consists of a part that filters out other users who have different preferences by calculating preferences using the attribute information of users and a part that finds a moving trajectory close to that of a user with a pattern-matching scheme. To verify the superiority of the proposed scheme, we compare it with existing schemes through various performance evaluations.

• KIISE Transactions on Computing Practices
• /
• v.22 no.8
• /
• pp.375-380
• /
• 2016

The proliferation of online social networking services (OSNSs) and smartphones has enabled people to easily make friends with a large number of users in the online communities, and interact with each other. This leads to an increase in the usage rate of OSNSs. However, individuals who have immersed into their digital lives, prioritizing the virtual world against the real one, become more and more isolated in the physical world. Thus, their socialization processes that are undertaken only through lots of face-to-face interactions and trial-and-errors are apt to be neglected via 'Add Friend' kind of functions in OSNSs. In this paper, we present a friend recommendation system based on the on/off-line contextual information for the OSNS users to have more serendipitous offline interactions. In order to accomplish this, we modeled both offline information (i.e., place visit history) collected from a user's smartphone on a 3D tensor, and online social data (i.e., friend relationships) from Facebook on a matrix. We then recommended like-minded people and encouraged their offline interactions. We evaluated the users' satisfaction based on a real-world dataset collected from 43 users (12 on-campus users and 31 users randomly selected from Facebook friends of on-campus users).

• The Journal of the Korea Contents Association
• /
• v.16 no.4
• /
• pp.56-64
• /
• 2016

With the development of information technologies and the wide spread of smart devices, the number of users of social network services has increased exponentially. Studies that identify user preferences and recommend similar users in these social network services have been actively done. In this paper, we propose a new scheme to recommend social network friends with similar preferences through the moving pattern analysis of mobile users. The proposed scheme removes the meaningless trajectories via companions, short time trajectories, and repeated trajectories to determine the correct user preference. The proposed scheme calculates user similarity using the meaningful trajectories and recommends users with similar preferences as friends. It is shown through performance evaluation that the proposed scheme outperforms the existing schemes.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.1
• /
• pp.111-118
• /
• 2014

Current friend recommendation system on Twitter primarily recommends the most influential twitter. However, this way of recommendation has drawbacks where it does not recommend the users of which attributes of interests are similar to theirs. Since users want other users of which attributes are similar, this study implements follower recommendation system based on the similarity of twitter node informations. The data in this study is from SNAP(Stanford Network Analysis Platform), and it consists of twitter node information of which number of followers is over 10,000 and twitter link information. We used the SNAP data as a training data, and generated a classifier which recommends and predicts the relation between followers. We evaluated the classifier by 10-Fold Cross validation. Once two twitter node informations are given, our model can recommend the relationship of the two twitters as one of following such as: FoFo(Follower Follower), FoFr(Follower Friend), NC(Not Connected).

• Proceedings of the Korea Contents Association Conference
• /
• 2015.05a
• /
• pp.45-46
• /
• 2015

최근 모바일 단말기의 보급과 함께 소셜 네트워크 서비스의 사용자 수가 급격하게 증가하고 있다. 본 논문에서는 사용자에게 의미 없는 장소를 판별하고 새로운 이동 궤적을 생성하여 유사한 사용자를 추천하는 친구 추천 기법을 제안한다. 성능평가를 통해 제안하는 기법이 기존 기법에 비해 성능이 우수함을 보인다.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.10 no.1
• /
• pp.61-71
• /
• 2014

Social networking services have changed the way people communicate. Rapid growth of information generated by social networking services requires effective search methods to give useful results. Over the last decade, social search methods have rapidly evolved. Traditional techniques become unqualified because they ignore social relation data. Existing social recommendation approaches consider social network structure, but social context has not been fully considered. Especially, the friend recommendation is an important feature of SNSs. People tend to trust the opinions of friends they know rather than the opinions of strangers. In this paper, we propose a levelized data processing method for social search in ubiquitous environment. We study previous researches about social search methods in ubiquitous environment. Our method is a new paradigm of levelelized data processing method which can utilize information in social networks, using location and friendship weight. Several experiments are performed and the results verify that the proposed method's performance is better than other existing method.

• The Journal of the Korea Contents Association
• /
• v.13 no.10
• /
• pp.547-555
• /
• 2013

This study aims to examine association between festival service evaluation attribute and behavior intention of visitors and research satisfaction with festival, second visit and recommendation intention, ultimately in order to suggest measures to establish market strategies. The study was conducted as follows. First, a total of 360 pieces of questionnaire were distributed from October 14 to 16, 2011 and a total of 335 pieces were collected. Except 15 pieces without responses, 320 were used for the study. Second, in service evaluation elements, program, facility and performance review had positive impacts on the satisfaction and second visit. All evaluation elements also positively affected recommendation intention. Third, in association between demographic features and satisfaction, second visit and recommendation intention, while the satisfaction positively influenced bringing a friend, it negatively influenced academic background and income. In addition, residence and job gave a positive affect on second visit, while income, bringing family and first visit gave a negative effect on the second visit. Last, age, academic background, income and bringing family gave a negative effect on recommendation intention.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.951-952
• /
• 2023

소위 '인생네컷'이라 불리는 셀프사진관은 MZ 세대의 새로운 놀이 문화로 떠오르며 사용자 수가 나날이 증가하고 있다. 그러나 짧은 시간 내에 다양한 포즈를 취해야 하는 셀프사진관 특성상 촬영이 낯선 사람에게는 여전히 진입장벽이 존재한다. 더불어 매번 비슷한 포즈와 사진 결과물에 기존 사용자는 점차 흥미를 잃어가는 문제점도 발생하고 있다. 이에 본 연구에서는 셀프사진관 사용자의 관계를 분류하는 모델을 개발하여 관계에 따른 적합하고 다양한 포즈를 추천하는 프레임워크를 제안한다. 사용자의 관계를 'couple', 'family', 'female_friend', 'female_solo', 'male_friend', 'male_solo' 총 6 개로 구분하였고 실제 현장과 유사하도록 단색 배경의 이미지를 우선으로 학습 데이터를 수집하여 모델의 성능을 높였다. 모델 학습 단계에서는 모델의 성능을 높이기 위해 여러 CNN 기반의 모델을 전이학습하여 각각의 정확도를 비교하였다. 결과적으로 195 장의 test_set 에서 accuracy 0.91 의 성능 평가를 얻었다. 본 연구는 객체 인식보다 객체 간의 관계를 학습시켜 관계성을 추론하고자 하는 것을 목적으로, 연구 결과가 희박한 관계 분류에 대한 주제를 직접 연구하여 추후의 방향성이나 방법론과 같은 초석을 제안할 수 있다. 또한 관계 분류 모델을 CCTV 에 활용하여 미아 방지 혹은 추적과 구조 등에 활용하여 국가 치안을 한층 높이는 데 기대할 수 있다.