Temporal Meta-path Guided Explainable Recommendation (WSDM2021)

Yicong Li

Last update: Nov 30, 2022

Related tags

Recommender Systems TMER

Overview

Temporal Meta-path Guided Explainable Recommendation (WSDM2021)

TMER

Code of paper "Temporal Meta-path Guided Explainable Recommendation".

Requirements

python==3.6.12
networkx==2.5
numpy==1.15.0
pandas==1.0.1
pytorch==1.0.0
pytorch-nlp==0.5.0
gensim==3.8.3

You can also install the environment via requirements.txt and environment.yaml.

Data Preparation

The original data can be found in the amazon data website.

For example, the meta_Musical_Instruments.json of Amazon_Music can be found here. The user_rate_item.csv in the code is here (ratings only).

Usage

If you want to change the dataset, you can modify the name in the code.

1.process data (You can ignore this step, if you just want to check TMER.)

python data_process.py

2.learn the user and item representations

python data/path/embed_nodes.py

3.learn the item-item path representations

python data/path/user_history/item_item_representation.py

4.learn the user-item path representations

python data/user_item_representation.py

5.generate user-item and item-item meta-path instances and learn their representations

python data/path/generate_paths.py
python data/path/user_history/meta_path_instances_representation.py

6.sequence item-item paths for each user

python data/path/user_history/user_history.py

7.run the recommendation

python run.py

Cite

If you find this code useful in your research, please consider citing:

@article{chen2021temporal,
  title={Temporal Meta-path Guided Explainable Recommendation},
  author={Chen, Hongxu and Li, Yicong and Sun, Xiangguo and Xu, Guandong and Yin, Hongzhi},
  journal={arXiv preprint arXiv:2101.01433},
  year={2021}
}

@inproceedings{10.1145/3437963.3441762,
	author = {Chen, Hongxu and Li, Yicong and Sun, Xiangguo and Xu, Guandong and Yin, Hongzhi},
	title = {Temporal Meta-Path Guided Explainable Recommendation},
	year = {2021},
	booktitle = {Proceedings of the 14th ACM International Conference on Web Search and Data Mining},
	pages = {1056–1064}
}

Comments

A question about deepwalk

In data_process.py, you use all the user-item relations (both the training set and the test set) to build graphs and embed nodes(in embed_nodes.py). However, I'm now questioning this process since the user-item relations of the test set are also included. Looking forward to your reply and thank you a lot.

opened by JiayinZheng 1
Missing file

Hi there, the following file is missing from the repo:

No such file or directory: 'data/Amazon_Music/representations/user_item_dic.wv'

Where can it be located? Thanks

opened by eperrier 1
code publication

Hello, I notice your work had been accepted by WSDM2021. So, would you please be nice to public your code and data? It seems to be helpful for my project. I would cite your paper appropriately in my work. Thank.

opened by JianhuanZhuo 1
A question about how you calculate the metrics

When calculating the metrics in run.py, the variable pos_id is always 100 (equals to N). However, according to the concept of Hit_rate, the pos_id here should be the index of the postive item or the ground truth item. Hence, I think that the code should be "pos_id = p_and_n_seq[N][2]". Otherwise, the pos_id will always be 100.

opened by JiayinZheng 0

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Temporal Meta-path Guided Explainable Recommendation (WSDM2021)

Related tags

Overview

Temporal Meta-path Guided Explainable Recommendation (WSDM2021)

TMER

Requirements

Data Preparation

Usage

Cite

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Comments

A question about deepwalk

Missing file

code publication

A question about how you calculate the metrics

Owner

Yicong Li

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