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With the growing popularity of location-based social networks, numerous location visiting records (e.g., check-ins) continue to accumulate over time. The more these records are collected, the better we can understand users’ mobility patterns and the more accurately we can predict their future locations. However, due to the personality trait of neophilia, people also show propensities of novelty seeking in human mobility, that is, exploring unvisited but tailored locations for them to visit. As such, the existing prediction algorithms, mainly relying on regular mobility patterns, face severe challenges because such behavior is beyond the reach of regularity. As a matter of fact, the prediction of this behavior not only relies on the forecast of novelty-seeking tendency but also depends on how to determine unvisited candidate locations. To this end, we put forward a Collaborative Exploration and Periodically Returning model …