IRMoGA

The source codes of 'Inferring Real Mobility in Presence of FakeCheck-ins Data' (IRMoGA)

Original Datasets

We conduct all experiments on two publicly available datasets: Foursquare and Gowalla. The source data can be downloaded from:

[1] Yang, D. Zhang, V. W. Zheng, and Z. Yu, “Modeling user activitypreference by leveraging user spatial temporal characteristics in LBSNs,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45,no. 1, pp. 129–142, 2014.

[2] Q. Yuan, G. Cong, Z. Ma, A. Sun, and N. M. Thalmann, “Time-aware point-of-interest recommendation,” inProceedings of the 36thinternational ACM SIGIR conference on Research and development ininformation retrieval, 2013, pp. 363–372.

**Notably, If you want to use our preprocessed data, please also cite thier great works.

**Notably, OSM and Yelp datastes are tackled by ourself.

Our Datasets

'data'--->training and testing data

'embeddings'--->POI embeddings

Environments

#python 3.7

#Tensorflow 2.x

#GPU RTX 3090 24G

Usage

e.g., ======>>'python IRMoGA_gw.py'

Notes

Any comments are appreciated.

Cites

Qiang Gao, Hongzhu Fu, Kunpeng Zhang, Goce Trajcevski, Xu Teng, Fan Zhou, "Inferring Real Mobility in Presence of FakeCheck-ins Data".