Mining user check-in features for location classification in location-based social networks

With the increasing popularity of location-based social networks, a large number of users have been involved in the check-ins. The venues where the user frequently repeats check-ins tend to play a very important role in his daily life, as they not only dominate the user's mobility behavior but also imply the user's personal preferences. Therefore, fast discerning of such check-in venues could enable us to improve a wide range of location-based services. In this paper, we propose a new location classification problem for users of location-based social networks, in which we aim to discern, given the observation that a user makes a "new" check-in at a venue, whether he will frequently repeat check-ins at this venue. To solve the problem, we first extract 16 features attached to the user's "new" check-ins. With the publicly available check-in dataset, we then train a location classifier based on Support Vector Machine and compare it with two baselines based on majority voting. The comparison results demonstrate the practicability of the trained location classifier.

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