# PPR-master

**Repository Path**: mcdragon/PPR-master

## Basic Information

- **Project Name**: PPR-master
- **Description**: This is an implementation of the POI recommendation model-PPR.
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-10-13
- **Last Updated**: 2021-10-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PPR-master
The implementation of the PPR model(Personalized POI Recommendation).

Paper ```Personalized POI Recommendation:Spatio-Temporal Representation Learning with Social Tie``` was accepted by DASFAA 2021.

# Usage:
## Install dependencies 
```pip install -r requirements.txt```
## Installation
Clone this repo.
```
git clone https://github.com/dsj96/PPR-master
cd PPR-master
chmod u+x train_PPR.sh
```
## Function
1. ```gen_graph.py``` file is used for heterogeneous graph construction. Parameter ```theta``` is $\theta$ in Equ.2, and ```epsilon``` is $\varepsilon$ in Equ.6.
2. ```reconstruct.cpp``` file is used for densifying graph. Parameter ```-threshold``` is $\rho$.
3. ```line.cpp``` file is used for learning latent representations. Parameter ```-size``` is embedding dim $d$.
4. ```train.py``` file is used for training and evaluating the spatio-temporal neural network. Parameter ```DELT_T``` is the time constraint $\tau$, and ```INPUT_SIZE/2``` is the embedding dim $d$. You could also change the ```HIDDEN_SIZE, EPOCH, LR, LAYERS OR TEST_SAMPLE_NUM```. 

# Data
In our experiments, the Foursquare datasets are from https://sites.google.com/site/dbhongzhi/. And the Gowalla and Brightkite dataset are from https://snap.stanford.edu/data/loc-gowalla.html and http://snap.stanford.edu/data/loc-Gowalla.html.
## Data Format
We utilize the first 80% chronological check-ins of each user as the training set, the remaining 20% as the test data.

train_checkin_file.txt and test_checkin_file.txt :
```<USER ID> \t <CHECKIN TIME> \t <POI ID> \t <LONGITUDE> \t <LATITUDE>```

friendship_file.txt : ```<USER ID>,<USER ID>```

# Training
You can train and evaluate the model by: ```./train_PPR.sh```

Or you can run the specific program file separately, but the parameters should be reasonable (Although we have set some default parameters).
```
eg: 
python3 gen_graph.py --input_path dataset/toyset/ --epsilon 0.5 --theta 24.0
python3 train.py --input_path dataset/toyset/ --input_size 16 --hidden_size 16 --layers 2 --lr 0.001 --delt_t 6.0 --epochs 20 --dr 0.2 --seed 1 --test_sample_num 300
```

# Cite
Please cite our paper if you find this code useful for your research:
```
@inproceedings{dai2021personalized,
  title={Personalized POI Recommendation: Spatio-Temporal Representation Learning with Social Tie.},
  author={Dai, Shaojie and Yu, Yanwei and Fan, Hao and Dong, Junyu},
  booktitle={DASFAA (1)},
  pages={558--574},
  year={2021}
}
```