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With the widespread use of mobile phones, users can share their location anytime, anywhere, as a form of check-in data. These data reflect user preferences. Furthermore, the preference rules for different users vary. How to discover a users preference from their related information and how to validate whether a preference model is suited to a user is important for providing a suitable service to the user. This study provides four main contributions. First, multiple preference models from different views for each user are constructed. Second, an algorithm is proposed to validate whether a preference model is applicable to the user by calculating the stability value of the users long-term check-in data for each model. Third, a unified model, i.e., a multi-channel convolutional neural network is used to characterize this applicability. Finally, three datasets from multiple sources are used to verify the validity of the method, the results of which show the effectiveness of the method.
This paper contributes to the human-machine interface community in two ways: as a critique of the closed-loop AC (augmented cognition) approach, and as a way to introduce concepts from complex systems and systems physiology into the field. Of particu
We present an approach utilizing Topological Data Analysis to study the structure of face poses used in affective computing, i.e., the process of recognizing human emotion. The approach uses a conditional comparison of different emotions, both respec
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People increasingly wear smartwatches that can track a wide variety of data. However, it is currently unknown which data people consume and how it is visualized. To better ground research on smartwatch visualization, it is important to understand the