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Human mobility data accumulated from Point-of-Interest (POI) check-insprovides great opportunity for user behavior understanding. However, dataquality issues (e.g., geolocation information missing, unreal check-ins, datasparsity) in real-life mobility data limit the effectiveness of existingPOI-oriented studies, e.g., POI recommendation and location prediction, whenapplied to real applications. To this end, in this paper, we develop a model,named Bi-STDDP, which can integrate bi-directional spatio-temporal dependenceand users' dynamic preferences, to identify the missing POI check-in where auser has visited at a specific time. Specifically, we first utilizebi-directional global spatial and local temporal information of POIs to capturethe complex dependence relationships. Then, target temporal pattern incombination with user and POI information are fed into a multi-layer network tocapture users' dynamic preferences. Moreover, the dynamic preferences aretransformed into the same space as the dependence relationships to form thefinal model. Finally, the proposed model is evaluated on three large-scalereal-world datasets and the results demonstrate significant improvements of ourmodel compared with state-of-the-art methods. Also, it is worth noting that theproposed model can be naturally extended to address POI recommendation andlocation prediction tasks with competitive performances.

Modelling of Bi-directional Spatio-Temporal Dependence and Users' Dynamic Preferences for Missing POI Check-in Identification