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MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

This is our Pytorch implementation for the paper:

Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua(2019). MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. In ACM MM`19, NICE, France,Oct. 21-25, 2019
Author: Dr. Yinwei Wei (weiyinwei at hotmail.com)

Introduction

Multi-modal Graph Convolution Network is a novel multi-modal recommendation framework based on graph convolutional networks, explicitly modeling modal-specific user preferences to enhance micro-video recommendation. We update the code and use the full-ranking strategy for validation and testing.

Citation

If you want to use our codes and datasets in your research, please cite:

@inproceedings{MMGCN,
  title     = {MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video},
  author    = {Wei, Yinwei and 
               Wang, Xiang and 
               Nie, Liqiang and 
               He, Xiangnan and 
               Hong, Richang and 
               Chua, Tat-Seng},
  booktitle = {Proceedings of the 27th ACM International Conference on Multimedia},
  pages     = {1437--1445},
  year      = {2019}
}

Environment Requirement

The code has been tested running under Python 3.5.2. The required packages are as follows:

• Pytorch == 1.1.0
• torch-cluster == 1.4.2
• torch-geometric == 1.2.1
• torch-scatter == 1.2.0
• torch-sparse == 0.4.0
• numpy == 1.16.0

Example to Run the Codes

The instruction of commands has been clearly stated in the codes.

• Kwai dataset
  python main.py --model_name='MMGCN' --l_r=0.0005 --weight_decay=0.1 --batch_size=1024 --dim_latent=64 --num_workers=30 --aggr_mode='mean' --num_layer=2 --concat=False
• Tiktok dataset
  python main.py --model_name='MMGCN' --l_r=0.0005 --weight_decay=0.1 --batch_size=1024 --dim_latent=64 --num_workers=30 --aggr_mode='mean' --num_layer=2 --concat=False
• Movielens dataset
  python main.py --model_name='MMGCN' --l_r=0.0001 --weight_decay=0.0001 --batch_size=1024 --dim_latent=64 --num_workers=30 --aggr_mode='mean' --num_layer=2 --concat=False

Some important arguments:

• model_name: It specifies the type of model. Here we provide three options:

  1. MMGCN (by default) proposed in MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video, ACM MM2019. Usage: --model_name='MMGCN'
  2. VBPR proposed in VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback, AAAI2016. Usage: --model_name 'VBPR'
  3. ACF proposed in Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention , SIGIR2017. Usage: --model_name 'ACF'
  4. GraphSAGE proposed in Inductive Representation Learning on Large Graphs, NIPS2017. Usage: --model_name 'GraphSAGE'
  5. NGCF proposed in Neural Graph Collaborative Filtering, SIGIR2019. Usage: --model_name 'NGCF'

• aggr_mode It specifics the type of aggregation layer. Here we provide three options:

  1. mean (by default) implements the mean aggregation in aggregation layer. Usage --aggr_mode 'mean'

  2. max implements the max aggregation in aggregation layer. Usage --aggr_mode 'max'

  3. add implements the sum aggregation in aggregation layer. Usage --aggr_mode 'add'

• concat: It indicates the type of combination layer. Here we provide two options:

  1. concat(by default) implements the concatenation combination in combination layer. Usage --concat 'True'
  2. ele implements the element-wise combination in combination layer. Usage --concat 'False'

Dataset

We provide three processed datasets: Kwai, Tiktok, and Movielnes.

• You can find the full version of recommendation datasets via Kwai, Tiktok, and Movielens. Since the copyright of datasets, we cannot release them directly. To facilate the line of research, we provide some toy datasets[BaiduPan](code: zsye) or [GoogleDriven]. Anyone needs the full datasets, please contact the owner of datasets.

-train.npy Train file. Each line is a user with her/his positive interactions with items: (userID and micro-video ID)
-val.npy Validation file. Each line is a user several positive interactions with items: (userID and micro-video ID)
-test.npy Test file. Each line is a user with several positive interactions with items: (userID and micro-video ID)

Copyright (C) <year> Shandong University

This program is licensed under the GNU General Public License 3.0 (https://www.gnu.org/licenses/gpl-3.0.html). Any derivative work obtained under this license must be licensed under the GNU General Public License as published by the Free Software Foundation, either Version 3 of the License, or (at your option) any later version, if this derivative work is distributed to a third party.

The copyright for the program is owned by Shandong University. For commercial projects that require the ability to distribute the code of this program as part of a program that cannot be distributed under the GNU General Public License, please contact weiyinwei@hotmail.com to purchase a commercial license.