Name
Abstract | ||
|---|---|---|
Location-Based Social Networks (LBSNs) have built bridges between virtual space and real-world mobility in recent years. The massive check-in data generated in LBSNs has made it possible to predict users' future check-in location, which has proved meaningful for e-commerce developments. Existing studies mainly focus on predicting the next check-in location with a coarse granularity, which shows limited performance in practical scenarios. In this paper, we propose a comprehensive approach based on user mobility pattern for predicting users' future check-in location at any fine-grained time in LBSNs. Firstly, user mobility patterns involving time periodicity, global popularity and user preference are analyzed. Then, a set of predictive features are extracted. Finally, the features are combined into a supervised scoring model and a classification model respectively in order to predict users' future check-in location. Extensive experiments on three real-world datasets demonstrate the efficiency and superiority of the proposed approach in various metrics. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CBD.2017.48 | 2017 Fifth International Conference on Advanced Cloud and Big Data (CBD) |
Keywords | Field | DocType |
LBSN,Location Prediction,Mobility Pattern,Scoring Model,Classification Model | Data mining,Social network,Computer science,Popularity,Mobility model,Granularity,Location prediction,Virtual space | Conference |
ISSN | ISBN | Citations |
2573-301X | 978-1-5386-1073-2 | 3 |
PageRank | References | Authors |
0.37 | 14 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiuxin Cao | 1 | 147 | 25.13 |
| Shuai Xu | 2 | 27 | 10.78 |
| Xuelin Zhu | 3 | 5 | 1.74 |
| Renjun Lv | 4 | 3 | 0.37 |
| Bo Liu | 5 | 6 | 5.82 |