Personalized location prediction for group travellers from spatial–temporal trajectories

In recent years, research on location predictions by mining trajectories of users has attracted a lot of attention. Existing studies on this topic mostly focus on individual movements, considering the trajectories as solo movements. However, a user usually does not visit locations just for the personal interest. The preferences of a travel group have significant impacts on the places they have visited. In this paper, we propose a novel personalized location prediction approach which further takes into account users’ travel group type. To achieve this goal, we propose a new group pattern discovery approach to extract the
travel groups from spatial-temporal trajectories of users. Type of the discovered groups, then, are identified through utilizing the profile information of the group members. The core idea underlying our proposal is the discovery of significant movement patterns of users to capture frequent movements by considering
the group types. Finally, the problem of location prediction is formulated as an estimation of the probability of a given user visiting a given location based on his/her current movement and his/her group type. To the best of our knowledge, this is the first work on location prediction based on trajectory pattern mining that investigates the influence of travel group type. By means
of a comprehensive evaluation using various datasets, we show that our proposed location prediction framework significantly achieve the higher performance than previous location prediction methods.