| Summary: | 碩士 === 國立交通大學 === 網路工程研究所 === 102 === With the developing of technologies about networks, there are more and more companies provide social media service. In service providers’ view, more customers lead to more income. How to explore new customers has become a significant issue. We call the people with high tendency to join a specific service as potential users. All the information about potential users comes from their friends. In the real world, people were often influenced by their friends. As a result, analyzing friends’ interaction behavior logs offer an unique way to explore potential users.
In this paper, we extract explicit features based on friends’ interaction behavior. Moreover, people tend to organize their own community in their life, we extract community based implicit features for a deeper exploration. To select effective predictors, we do some observation for choosing discriminative feature set. After exploring the effective predictor, we use different classifiers to predict the potential users and compare their effectiveness. Finally, we conduct our method in real dataset and show that the features we extract can reach about 70% accuracy.
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