MMGCN：用于微视频个性化推荐的多模态图卷积网络_Python_下载.zip

# 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](https://arxiv.org/abs/1510.01784), AAAI2016. Usage: `--model_name 'VBPR'` 3. `ACF` proposed in [Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention ](https://dl.acm.org/citation.cfm?id=3080797), SIGIR2017. Usage: `--model_name 'ACF'` 4. `GraphSAGE` proposed in [Inductive Representation Learning on Large Graphs](https://arxiv.org/abs/1706.02216), NIPS2017. Usage: `--model_name 'GraphSAGE'` 5. `NGCF` proposed in [Neural Graph Collaborative Filtering](https://arxiv.org/abs/1905.08108), 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](https://www.kuaishou.com/activity/uimc), [Tiktok](http://ai-lab-challenge.bytedance.com/tce/vc/), and [Movielens](https://grouplens.org/datasets/movielens/). Since the copyright of datasets, we cannot release them directly. To facilate the line of research, we provide some toy datasets[[BaiduPan](https://pan.baidu.com/s/1BODXP7iihw8qtxpLeEv_XA)](code: zsye) or [[GoogleDriven]](https://drive.google.com/file/d/1NoisyVDFWykTszSIbHdeoBrKn0t-D0ps/view?usp=sharing). Anyone needs the full datasets, please contact the owner of datasets. ||#Interactions|#Users|#Items|Visual|Acoustic|Textual| |:-|:-|:-|:-|:-|:-|:-| |Kwai|1,664,305|22,611|329,510|2,048|-|100| |Tiktok|726,065|36,656|76,085|128|128|128| |Movielens|1,239,508|55,485|5,986|2,048|128|100| -`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.