With rapid growth of the GPS enabled mobile device, location-based online social network services become more and more popular, and allow their users to share life experiences with location information. In this paper, we considered a method for recommending places to a user based on spatial databases of location-based online social network services. We used a user-based collaborative filtering method to make a set of recommended places. In the proposed method, we calculate similarity of users' check-in activities not only their positions but also their semantics such as "shopping", "eating", "drinking", and so forth. We empirically evaluated our method in a real database and found that it outperforms the naive singular value decomposition collaborative filtering recommendation by comparing the prediction accuracy.
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