Gun-Woo Park

To Enhance Web Search Based on Topic Sensitive_Social Relationship Ranking Algorithm in Social Networks

Social Network Services(SNS) such as Facebook, Friendster, MySpace and Orkut have established themselves as very popular and powerful tools for making and finding friends and for identifying other people who share similar interests. Search behavior of Web users often reflects that of others who have similar interests or similar information profiles in social networks. Therefore, if we locate web users interested in certain topics or areas and then keep track of their preferences in terms of Web search results, we can pinpoint relevant and reliable information about these people in a timely manner In this paper, we present an efficient search system called SMART Finder. This system supports efficient search results for locating people whose social relationships are highly ranked according to specific topics. It can also identify people who are highly associated with each other with regard to Web search topics. Our results are supported by experiments on Orkut and Facebook.

Personalized and Social Search by Finding User Similarity based on Social Networks

Social Networks which is composed of network with an individual in the center in a web support mutual-understanding of information by searching user profile and forming new link. Therefore, if we apply the Social Network which consists of web users who have similar immanent information to web search, we can improve efficiency of web search and satisfaction of web user about search results. In this paper, first, we make a Social Network using web users linked directly or indirectly. Next, we calculate Similarity among web users using their immanent information according to topics, and then reconstruct Social Network based on varying Similarity according to topics. Last, we compare Similarity with Search Pattern. As a result of this test, we can confirm a result that among users who have high relationship index, that is, who have strong link strength according to personal attributes have similar search pattern. If such fact is applied to search algorithm, it can be possible to improve search efficiency and reliability in personalized and social search.

Mobbing-Value Algorithm based on User Profile in Online Social Network

Mobbing is not restricted to problem of young people but the bigger recent problem occurs in workspaces. According to reports of ILO and domestic case mobbing in the workplace is increasing more and more numerically from 9.1%(`03) to 30.7%(`08). These mobbing brings personal and social losses. The proposed algorithm makes it possible to grasp not only current mobbing victims but also potential mobbing victims through user profile and contribute to efficient personnel management. This paper extracts user profile related to mobbing, in a way of selecting seven factors and fifty attributes that are related to this matter. Next, expressing extracting factors as `1` if they are related me or not `0`. And apply similarity function to attributes summation included in factors to calculate similarity between the users. Third, calculate optimizing weight choosing factors included attributes by applying neural network algorithm of SPSS Clementine and through this summation Mobbing-Value(MV) can be calculated . Finally by mapping MV of online social network users to G2 mobbing propensity classification model(4 Groups; Ideal Group of the online social network, Bullies, Aggressive victims, Victims) which is designed in this paper, can grasp mobbing propensity of users, which will contribute to efficient personnel management.