The datasets are from the work "Quan Yuan, Gao Cong, Zongyang Ma, Aixin Sun, Nadia Magnenat-Thalmann: Time-aware point-of-interest recommendation. SIGIR 2013.", and can be downloaded from this link :

• Foursquare
• Gowalla

• BPR
• GRU
• FPMC-LR: Yang, Rui, Run Zhao, and Dong Wang. "Successive Point-of-Interest Recommendation in Intelligent Business Area." (2015).
• PRME: Feng, Shanshan, et al. "Personalized Ranking Metric Embedding for Next New POI Recommendation." IJCAI. Vol. 15. 2015.
• Poi2Vec: Feng, Shanshan, et al. "POI2Vec: Geographical Latent Representation for Predicting Future Visitors." AAAI. 2017.
• (Unmodified) GEOIE: Wang, Hao, et al. "Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation." IJCAI. 2018.
• Distance2Pre: Q Cui, Y Tang, S Wu, L Wang. "Distance2Pre: Predict the Next Point-of-Interest via Mining Personalized Spatial Preference". 2019.

1. Download datasets to ./poidata/Foursquare or ./poidata/Gowalla respectively
2. Run the file ./poidata/extract_whole_user_buys.py to extract poi sequences
3. Run the file prog_xxx.pyto train specific model.