Predicting social network users'out-of-town mobility based on their generated spatio-temporal data has become an impending demand for urban collaborative management.User out-of-town mobility is indeed a"long tail"event compared with user mobility inside a city,resulting in extremely sparse check-in data generated by users in the out-of-town area.It is difficult for existing studies to utilize the limited cross-city check-in data to model users'out-of-town mobility preference,and afterwards accurately predict users'mobility outside the city.Toward these issues,in this article,a novel intention-aware framework called T1EMPO(short for intenTIon-awarE Mobility Preference mOdeling)for modeling user out-of-town mobility preference is proposed and implemented.Firstly,in order to alleviate the data sparsity problem,abundant trajectories are sampled from the constructed out-of-town location network via random walk,based on which a specific number of user intentions for out-of-town mobility can be discovered through unsupervised clustering.Secondly,the memory network is introduced to refine user intentions from similar users'out-of-town trajectories.Thirdly,following the idea of transfer learning,user check-ins inside a city and user intentions outside a city are interactively modeled to enhance user's out-of-town mobility preference representation.Finally,the probability of a user visiting an out-of-town location is quantified by integrating the user's out-of-town mobility preference representation and the location hidden representation.Extensive experiments based on multiple cross-city check-in datasets are conducted,and empirical results indicate that the proposed TIEMPO framework can effectively predict users'out-of-town mobility in terms of the visited locations,where the prediction accuracy metric Acc@10 shows a significant advantage of 12％-15％and the ranking reliability metric NDCG@10 achieves 3％-5％advantage compared with the baseline models.Even in cold-start prediction scenarios,TIEMPO framework still has the best performance.
维普
万方数据