FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval of WWW'24

Xu Chen, Ph.D. student of Renming University of China, GSAI

Any question, please mail to xc_chen@ruc.edu.cn

Prerequisites

• Python 3
• TensorFlow-GPU >= 1.8 (< 2.0)
• pytorch
• Faiss-GPU
• The project is based on ComiRec github

Getting Started

Installation

• Install TensorFlow-GPU 1.x

• Install Faiss-GPU based on the instructions here: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md

Dataset

• Original links of datasets are:

  • http://jmcauley.ucsd.edu/data/amazon/index.html
  • https://tianchi.aliyun.com/dataset/dataDetail?dataId=649&userId=1

To fit the requirement of uploading, we only save the processed category files, for the processed dataset, please see ComiRec(KDD2020) https://github.com/THUDM/ComiRec

Training

Firstly, Training on the base retrieval model based on the following command

You can use python src/train.py --dataset {dataset_name} --model_type {model_name} to train a specific model on a dataset. Other hyperparameters can be found in the code. (If you share the server with others or you want to use the specific GPU(s), you may need to set CUDA_VISIBLE_DEVICES.)

For example, you can use python src/train.py --dataset book --model_type ComiRec-DR to train ComiRec-DR model on Book dataset.

Secondly, Process FairSync:

please run python src/FairSync.py

demo result: {'recall': 0.074, 'ndcg': 0.062, 'hitrate': 0.160, 'diversity': 0.208, 'ESP': 1.0}

For other parameters see:

args_name	type	description
dataset	str	Choose dataset in ["book", "taobao"]
model_type	str	The base model type, please make sure the model is trained in the first step
minimum_exposure	float	Each groups' required minimum exposures
topN	int	Retreival number, please make sure the model is trained in the first step
FairSync_lr	float	The dual updating learning rate
eval_batch_size	int	The performing batch size B in the paper

##For citation, please cite the following bib

@inproceedings{Xu-FairSync-WWW24,
author = {Xu, Chen and Jun Xu and Yiming Ding and Xiao Zhang and Qi Qi},
title = {FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval},
year = {2024},
isbn = {979-8-4007-0171-9/24/05},
publisher = {Association for Computing Machinery},
address = {Singapore, Singapore},
doi = {10.1145/3589334.3645413},
booktitle = {Proceedings of the ACM Web Conference 2024 (WWW '24)},
series = {WWW '24}
}