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Recent years have actually seen the increasing rise in popularity of Location-based Social Network (LBSN) services, which supplies unrivaled opportunities to build individualized Point-of-Interest (POI) recommender methods. Present POI recommendation and location prediction tasks use past information for future suggestion or forecast from an individual path viewpoint, while the missing POI group identification task has to make use of the check-in information both before and after the missing category. Therefore,