The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

Last update: Mar 13, 2022

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Deep Learning REST

Overview

REST

The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies.

Usage

Download dataset

Download datasets: Ciao, Epinions, Yelp

Then unzip them into the directory datasets and run preprocess_yelp.py.

└── datasets
    ├── Ciao
    │   ├── rating_with_timestamp.mat
    │   ├── trust.mat
    ├── Epinions
    │   ├── rating_with_timestamp.mat
    │   ├── trust.mat
    ├── Yelp
    │   ├── yelp_academic_dataset_review.json
    │   ├── yelp_academic_dataset_user.json
    │   ├── noiso_reid_u2uir.npz
    │   ├── ...

Run

    python ./run_rate/run_rest_rate_ciao.py

During the training, we can obtain some logs and model-checkpoints in the directory logs and saved_models,

Results

Model	Ciao RMSE	Epinions RMSE	Yelp RMSE
PMF	1.1936±0.0019	1.2755±0.0022	1.2454±0.0011
NeuMF	0.9828±0.0022	1.0838±0.0015	1.1958±0.0005
MultiVAE	1.1908±0.0014	1.2104±0.0039	1.2944±0.0020
RecVAE	1.1787±0.0022	1.1946±0.0038	1.2385±0.0014
CausE	1.0003±0.0013	1.0705±0.0013	1.2039±0.0015
CVIB-MF	1.2001±0.0011	1.2477±0.0003	1.3189±0.0024
CVIB-NCF	1.0462±0.0013	1.2477±0.0003	1.3613±0.0043
MACR-MF	1.1859±0.0030	1.2364±0.0031	1.2344±0.0004
DecRS	0.9875±0.0033	1.0617±0.0033	-
GraphRec	0.9743±0.0021	1.0567±0.0019	1.1968±0.0017
NGCF	1.0135±0.0010	1.1286±0.0017	1.2231±0.0017
LightGCN	1.1919±0.0014	1.2025±0.0005	1.2444±0.0019
REST	0.9635±0.0009	1.0413±0.0007	1.1733±0.0006

Detailed results can be found in the paper.

The PyTorch implementation of paper REST: Debiased Social Recommendation via Reconstructing Exposure Strategies

Related tags

Overview

REST

Usage

Download dataset

Run

Results

Owner

DMIRLAB

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