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" [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.

Requirements

• Python >= 3.6
• PyTorch = 1.0

How to run the code

1. Proprocess the data first:

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):

python train_transe_model.py --dataset <dataset_name>

3. Train RL agent:

python train_agent.py --dataset <dataset_name>

4. Evaluation

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.