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| ''' Mobile spatial and temporal data based user profiles identification''' | | ''' Mobile spatial and temporal data based user profiles identification''' | ||
| When people using their APPs in smartphones, the communication base station will catch the URL requests from each customer. Each record is assigned to a specific user ID, and includes a time stamp and an URL request. Moreover, most frequently used APPs need location service provided by assembled GPS sensor in the smartphone. | | When people using their APPs in smartphones, the communication base station will catch the URL requests from each customer. Each record is assigned to a specific user ID, and includes a time stamp and an URL request. Moreover, most frequently used APPs need location service provided by assembled GPS sensor in the smartphone, and the coordinates could be extract from the URLs. With the information above, each user could be modeled by a set of spatial-temporal data. Different users could show different patterns, which includes the features like POI preference, trajectory type, usage frequency at different time and etc. We aim to cluster the users in different categories according to their spatial-temporal data. | ||
| Basic data science knowledge, Python programming and data science related libraries,. | | Basic data science knowledge, Python programming and data science related libraries,. | ||
| Jiaquan Zhang (jzhang@cs.uni-goettingen.de) | | Jiaquan Zhang (jzhang@cs.uni-goettingen.de) | ||
| [https:// | | [https://arxiv.org/pdf/1711.04710.pdf] | ||
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