# DisenPOI

**Repository Path**: branchlets/DisenPOI

## Basic Information

- **Project Name**: DisenPOI
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-11-19
- **Last Updated**: 2023-11-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## DisenPOI

This is the pytorch implementation for our WSDM 2023 [paper](https://arxiv.org/abs/2210.16591):
> Yifang Qin, Yifan Wang, Fang Sun, Wei Ju, Xuyang Hou, Zhe Wang, Jia Cheng, Jun Lei and Ming Zhang(2022). 
> DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation

In this paper, we propose DisenPOI, a novel Disentangled dual-graph framework for POI recommendation.
DisenPOI jointly utilizes sequential and geographical relationships on two separate graphs and disentangles
the two influences with self-supervision.

### Environment Requirement

The code has been tested running under Python 3.8.13. The required packages are as follows:

- pytorch == 1.11.0
- torch_geometric == 2.0.4
- pandas == 1.4.1
- sklearn == 0.23.2

Please cite our paper if you use the code.