Regular behavior measure for location based services

We introduce a method that can measure the degree of regularity or irregularity of the behavior for enhancing the performance of location-based services (LBSs) such as check-in. It is still challenging for LBSs to determine the places to recommend that best suits the user's needs. Our aim is to identify the user's status (regular or irregular) of each check-in. Most previous studies approached this problem by acquiring usual locations (e.g., home or office) or assessing check-in frequency. We propose more effective measure by using a multinomial-distribution-based method that considers the periodic check-ins of the user on various time-scales. Our method can accurately identify irregular check-ins even in usual locations and we find that the users tend to continue irregular check-ins in a certain range of time.