Predicting user location is one of the most important topics in data mining. Although human mobility is reasonably predictable for frequently visited places, novel location prediction is much more difficult. However, location-based services (LBSs) can influence users' choice of destination and can be exploited to more accurately predict user location even for new locations. In this study, we assessed the behavior difference for specific LBS users and non-users by using large-scale check-in data. We found a remarkable difference between specific LBS users and non-users (e.g., check-in locations) that had previously not been revealed. Then, we proposed a location prediction method exploiting the characteristics of check-in locations and analyzed how specific LBS usage influences location predictability. We assumed that users who use the same LBS tend to visit similar locations. The results showed that the novel location predictability of specific LBS users is up to 43.9% higher than that of non-users.

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