Name
Title | ||
|---|---|---|
Next Check-in Location Prediction via Footprints and Friendship on Location-Based Social Networks |
Abstract | ||
|---|---|---|
With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/MDM.2018.00044 | 2018 19th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
Location-based social networks,Location Prediction,Historical Trajectories,Social Circle | Data mining,Data set,Collaborative filtering,Social network,Check-in,Friendship,Task analysis,Computer science,Social circle,Trajectory,Distributed computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-4134-7 | 1 | 0.34 |
References | Authors | |
14 | 5 | |