| Summary: | 碩士 === 國立交通大學 === 資訊科學與工程研究所 === 105 === With the explosion of smartphones, users are easy to share their current location and activities with their friends by the check-in function in location-based social networks. Moreover, the success of viral marketing in social networks since users are more likely to accept the information from their friends. To promote a target location attracting as more as possible users to visit in location-based social networks via viral marketing, we have to estimate the propagation probability from users’ check-in records. In this thesis, we focus on estimate propagation probability of each social connection. Prior works only consider the distance between users’ visited locations and the target location. However, different users have different category preferences. For instance, most users would like to move far away if the category of target location is preferred. Therefore, we consider not only the individual check-in behavior in different categories but also the individual category preferences. To derive the propagation probability in LBSNs, experiments are conducted on two real datasets, and the results show that our proposed approach can truly reflect the information propagation in LBSNs.
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