The SAE-NAD model for Point-of-Interest Recommendation
The implementation of the paper:
Chen Ma, Yingxue Zhang, Qinglong Wang, and Xue Liu, "Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence", in the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018)
Arxiv: https://arxiv.org/abs/1809.10770
Please cite our paper if you use our code. Thanks!
Author: Chen Ma (allenmc1230@gmail.com)
Environments
- python 3.6
- PyTorch (version: 0.4.0)
- numpy (version: 1.15.0)
- scipy (version: 1.1.0)
- sklearn (version: 0.19.1)
Dataset
In our experiments, the Foursquare and Yelp datasets are from http://spatialkeyword.sce.ntu.edu.sg/eval-vldb17/. And the Gowalla dataset is from https://snap.stanford.edu/data/loc-gowalla.html (if you need the data after preprocessing, please send me an email).
Example to run the codes
Data preprocessing:
Run the cal_poi_pairwise_relation.py to calculate the pairwise relations between locations, which will be stored in ./data/Foursquare/.
python cal_poi_pairwise_relation.py
Train and evaluate the model (you are strongly recommended to run the program on a machine with GPU):
python run.py