MUC
Next Check-ins Prediction via History and Friendship on Location-Based Social Networks (MDM 2018)
Performance
Details for Accuracy:
| Dataset | [email protected] | [email protected] | [email protected] |
| ---------- | ------------| -------------| ---------------|
| Foursquare | 0.8389 | 0.9105 | 0.9368 |
| Gowalla | 0.7522 | 0.846 | 0.8866 |
- The performance of our framework on Foursquare and Gowalla.
Requirements
- python==3.7
Datasets
We use two real-world LBSN datasets from Foursquare and Gowalla.
Statistics:
| Dataset | Number of users | Number of POIs | Number of check-ins | Number of social links |
| ---------- | --------------- | -------------- | ---------------------- |-------------------------|
| Foursquare | 11,326 | 182,968 | 1,385,223 | 47,164 |
| Gowalla | 107,092 | 1,280,969 | 6,442,890 | 950,327 |
- Foursquare_MUC: Foursquare contains check-in data ranging from January 2011 to July 2011.
- Gowalla_MUC: Gowalla includes check-in data between Feb. 2009 and Oct 2010.
How to run MUC model
1.python loc_prodict_Foursquare.py
2.python loc_prodict_Gowalla.py
Citation
Please cite our paper if you use the code or datasets:
@inproceedings{SuLTXH18,
title={Next Check-in Location Prediction via Footprints and Friendship on Location-Based Social Networks},
author={Yijun Su, Xiang Li, Wei Tang, Ji Xiang and Neng Gao},
booktitle={IEEE International Conference on Mobile Data Management, {MDM} 2018},
pages={251-256},
doi={10.1109/MDM.2018.00044},
year={2018}
}
Contact
If you have any questions, please contact us by [email protected], we will be happy to assist.
Last Update Date: November 18, 2021