| Summary: | 碩士 === 國立高雄大學 === 資訊管理學系碩士班 === 100 === In recent years, social networking sites have becoming an important platform for users to establish the relationship between each other. As time goes by, the links between people will form the so-called “Strong Links”. For those users, information provided by the friends with strong link is considered as more interesting and useful. Currently, most of search engines are designed based on only measuring the similarity between keywords and articles. However, the social relations between the authors of articles and searcher have not been taken into account. Therefore, in order to improve the performance of search engines, we include the measurement of social relationship into traditional search engine. We expect to improve the search quality and to enhance the satisfaction of search.
In this study, we will train the data from Facebook to calculate the social relationship and content. About the content, the data will be process by using CKIP and TFIDF. Finally, we proposed a social ranking value which combines traditional TF-IDF and the values of social relationship. The social ranking value will be used as the key to rank the search results. In this paper, we will also demonstrate a empirical example to explain the proposed methodology as well as the system interface. Comparing social search with TF-IDF search, we can conclude that the information provided by users’ friends are very important for users.
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