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With the widespread use of mobile phones, users can share their locationanytime, anywhere, as a form of check-in data. These data reflect userpreferences. Furthermore, the preference rules for different users vary. How todiscover a user's preference from their related information and how to validatewhether a preference model is suited to a user is important for providing asuitable service to the user. This study provides four main contributions.First, multiple preference models from different views for each user areconstructed. Second, an algorithm is proposed to validate whether a preferencemodel is applicable to the user by calculating the stability value of theuser's long-term check-in data for each model. Third, a unified model, i.e., amulti-channel convolutional neural network is used to characterize thisapplicability. Finally, three datasets from multiple sources are used to verifythe validity of the method, the results of which show the effectiveness of themethod.

Construction and Adaptability Analysis of User's Preference Models Based on Check-in Data in LBSN