Discovering Semantic Mobility Pattern from Check-in Data

The wealth of check-in data offers new opportunities for better understanding user movement patterns. Existing studies have been focusing on mining explicit frequent sequential patterns. However, the sparseness of check-in data makes it difficult that all explicit patterns be precisely discovered. In addition, due to the weakness in expressing semantic knowledge of explicit patterns, the need for discovering semantic pattern rises. In this paper, we propose the Topical User Transition Model (TUTM) to discover the semantic mobility patterns by analyzing topical transitions. Via this model, we can discover semantic transition properties and predict the user movement preferences. Furthermore, Expectation-Maximization (EM) algorithm incorporating with Forward- Backward algorithm is provided for estimating the model parameters. To demonstrate the performance of TUTM model, experimental studies are carried out and the results show that our model can not only reasonably explain user mobility patterns, but also effectively improve the prediction accuracy in comparison with traditional approaches.