# PGPR

**Repository Path**: greitzmann/PGPR

## Basic Information

- **Project Name**: PGPR
- **Description**: Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-04-15
- **Last Updated**: 2021-04-15

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
This repository contains the source code of the SIGIR 2019 paper "[Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://arxiv.org/abs/1906.05237)" [2].

## Datasets
Two Amazon datasets (Amazon_Beauty, Amazon_Cellphones) are available in the "data/" directory and the split is consistent with [1].
All four datasets used in this paper can be downloaded [here](https://drive.google.com/uc?export=download&confirm=Tiux&id=1CL4Pjumj9d7fUDQb1_leIMOot73kVxKB).

## Requirements
- Python >= 3.6
- PyTorch = 1.0


## How to run the code
1. Proprocess the data first:
```bash
python preprocess.py --dataset <dataset_name>
```
"<dataset_name>" should be one of "cd", "beauty", "cloth", "cell" (refer to utils.py).

2. Train knowledge graph embeddings (TransE in this case):
```bash
python train_transe_model.py --dataset <dataset_name>
```

3. Train RL agent:
```bash
python train_agent.py --dataset <dataset_name>
```

4. Evaluation
```bash
python test_agent.py --dataset <dataset_name> --run_path True --run_eval True
```
If "run_path" is True, the program will generate paths for recommendation according to the trained policy.
If "run_eval" is True, the program will evaluate the recommendation performance based on the resulting paths.

## References
[1] Yongfeng Zhang, Qingyao Ai, Xu Chen, W. Bruce Croft. "Joint Representation Learning for Top-N Recommendation with Heterogeneous Information Sources". In Proceedings of CIKM. 2017.

[2] Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, Yongfeng Zhang. "Reinforcement Knowledge Graph Reasoning for Explainable Recommendation." In Proceedings of SIGIR. 2019.