The datasets can be used for POI/next-POI recommendation, trajectory recommendation, friends recommendation (link prediction), activity recommendations, group recommendation and community discovery tasks.
Dataset Statistics used in FGCRec (see our paper FGCRec: Fine-Grained Geographical Characteristics Modeling for Point-of-Interest Recommendation for details):
| Dataset | Number of users | Number of POIs | Number of check-ins| User-POI matrix density|
| ----------------- | --------------- | -------------- | -------------------| ---------------------- |
| Foursquare_FGCRec | 7,642 | 28,484 | 512,523 | 0.13% |
| Gowalla_FGCRec | 5,628 | 31,803 | 620,683 | 0.22% |
Dataset Statistics used in FGRec (see our paper FGRec: A Fine-Grained Point-of-Interest Recommendation Framework by Capturing Intrinsic Influences for details):
| Dataset | Number of users | Number of POIs | Number of categories | Number of check-ins | Number of social links | User-POI matrix density|
| ---------------- | --------------- | -------------- | ---------------------| ---------------------- |-------------------------|----------------------- |
| Foursquare_FGRec | 2,551 | 13,474 | 10 | 124,933 | 32,512 |0.291% |
| Yelp_FGRec | 30,887 | 30,887 | 624 | 860,888 | 860,888 |0.14% |
Dataset Statistics used in CARec (see our paper CARec: Content-Aware Point-of-Interest Recommendation via Adaptive Bayesian Personalized Ranking for details):
| Dataset | Number of users | Number of POIs | Number of check-ins | Number of reviews | User-POI matrix density|
| ---------------- | --------------- | -------------- | ---------------------- |-------------------------|----------------------- |
| Foursquare_CARec | 9,728 | 12,449 | 177,142 | 234,793 |0.15% |
| Yelp_CARec | 5,577 | 6,900 | 518,186 | 542,707 |0.46% |
Dataset Statistics used in MUC (see our paper Next Check-ins Prediction via History and Friendship on Location-Based Social Networks for details):
| Dataset | Number of users | Number of POIs | Number of check-ins | Number of social links |
| -------------- | --------------- | -------------- | ---------------------- |-------------------------|
| Foursquare_MUC | 11,326 | 182,968 | 1,385,223 | 47,164 |
| Gowalla_MUC | 107,092 | 1,280,969 | 6,442,890 | 950,327 |
tips： we divide the dataset into training set, tuning set and test set in terms of the user’s check-in time. For each user, the earliest 70 % check-ins are selected as training data, the most recent 20 % check-ins as test data and the remaining 10 % as the tuning data.
-
Foursquare_FGCRec: Foursquare includes check-in data ranging from April 2012 to September 2013.
-
Gowalla_FGCRec: Gowalla contains check-in data ranging from February 2009 to October 2010.
-
Foursquare_FGRec: Foursquare includes the check-in data of users who live in California, ranging from December 2009 to June 2013.
-
Yelp_FGRec: Yelp contains a large number of geotagged businesses (also called POIs) and reviews within several cities.
-
Foursquare_CARec: We filter users who visited less than 10 POIs in Foursquare dataset and POIs visited by less than 10 users.
-
Yelp_CARec: https://www.yelp.com/dataset. We filter users who visited less than 32 POIs in Foursquare dataset and POIs visited by less than 31 users.
-
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.
Please cite our paper FGCRec if you use the datasets (Foursquare_FGCRec, Gowalla_FGCRec):
@inproceedings{suicc2020fgcrec,
title={FGCRec: Fine-Grained Geographical Characteristics Modeling for Point-of-Interest Recommendation},
author={Yijun Su, Xiang Li, Baoping Liu, Daren Zha, Ji Xiang, Wei Tang and Neng Gao},
booktitle={IEEE International Conference on Communications},
pages={1-6},
doi={10.1109/ICC40277.2020.9148797},
year={2020}
}
Please cite our paper FGRec if you use the datasets (Foursquare_FGRec, Yelp_FGRec):
@inproceedings{suijcnn2020fgrec,
title={FGRec: A Fine-Grained Point-of-Interest Recommendation Framework by Capturing Intrinsic Influences},
author={Yijun Su, Jia-Dong Zhang, Xiang Li, Daren Zha, Ji Xiang, Wei Tang and Neng Gao},
booktitle={IEEE International Joint Conference on Neural Networks, {IJCNN} 2020},
pages={1-9},
doi={10.1109/IJCNN48605.2020.9207571},
year={2020}
}
Please cite our paper CARec if you use the datasets (Foursquare_CARec, Yelp_CARec):
@article{LiuSZGX19,
title={CARec: Content-Aware Point-of-Interest Recommendation via Adaptive Bayesian Personalized Ranking},
author={Baoping Liu, Yijun Su, Daren Zha, Neng Gao, and Ji Xiang},
journal={Australian Journal of Intelligent Information Processing Systems}
volume= {15},
number= {3},
pages= {61--68},
year= {2019}
}
Please cite our paper MUC if you use the datasets (Foursquare_MUC, Gowalla_MUC):
@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}
}
If you have any questions, please contact us by [email protected], we will be happy to assist.
Last Update Date: December 13, 2021
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent