| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 100 === The social network phenomenon is with such strong momentum that it has resulted in the change of lifestyle. In this new lifestyle, social network sites have created new ways of socialization and interaction. Traditionally, researchers have often used statistical methods to analyze online social behaviors, but the research can lack authenticity since the social relationships are represented by the networking among the online user postings with common topics. The weighting of social network analysis is usually not taken into consideration. With such traditional methods of social network analysis, the key nodes found and networking factors identified tend to be oversimplified.
This research proposes a new method to improve the existing friends’ recommender systems of the social network sites. The recommender system consists of three methods: (1) social network analysis model, (2) text mining model, and (3) recommendation model. In such system, we calculate betweenness centrality and term frequency of online social network postings, and the estimated value is generated by the collaborative filtering model. Finally, we recommend the users with the highest degree of similarity on the same topic to achieve the purpose of our research.
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