toooooodo / MetaKG

Source code for MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation. TKDE 2022.

MetaKG

This is our Pytorch implementation for the paper:

Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang and Yunjun Gao (2022). MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation

Environment Requirements

• Ubuntu OS
• Python >= 3.8 (Anaconda3 is recommended)
• PyTorch 1.7+
• A Nvidia GPU with cuda 11.1+

Datasets

We user three popular datasets Amazon-book, Last-FM, and Yelp2018 to conduct experiments.

• We follow the paper "KGAT: Knowledge Graph Attention Network for Recommendation" to process data.
• In order to construct cold-start scenario, we find user registration time, item publication time or first interaction time in the full version of recommendation datasets. Then we divide new and old ones in chronological order.
• For Amazon-book, download book reviews (5-core) from here, put it into related rawdata folder.
• For Last-FM, download LFM-1b dataset from here, unzip and put it into related rawdata folder.
• For Yelp2018, download Yelp2018 version dataset from here, unzip and put it into related rawdata folder.

The prepared folder structure is like this:

- Datasets
    - pretrain
    - amazon-book
	- rawdata
		- reviews_Books_5.json.gz
    - last-fm
    	- rawdata
    		- LFM-1b_albums.txt
    		- LFM-1b_artists.txt
    		- ...
    - yelp2018
    	- rawdata
    		- yelp_academic_dataset_business.json
    		- yelp_academic_dataset_checkin.json
    		- ...

Train

1. Now, we have provided the cold-start scenario data of last-fm. The codes for constructing the other datasets is as follows.

   python construct_data.py

2. Start training

   Here, we have provided the "meta-model" after meta-training, so you can adapt directly to cold-start scenarios.

   python main.py --dataset last-fm --use_meta_model True

   You can also retrain the entire model.

   python main.py --dataset last-fm --use_meta_model False

Reference

• We use the codes of KGAT.
• You can find other baselines in Github.