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Personalized Hashtag Recommendation for Micro-videos

A novel Graph Convolution Network based Personalized Hashtag Recommendation (GCN-PHR) model, which leverages GCN techniques and attention mechanism to model complicated interactions among <users, hashtags, micro-videos>.

Authors

Yinwei Wei1, Zhiyong Cheng2, Xuzheng Yu1, Zhou Zhao3, Lei Zhu4, Liqiang Nie1*

1 Shandong University, China
2 Qilu University of Technology (Shandong Academy of Sciences), China
3 Zhejiang University, China
4 Shandong Normal University, China
* Corresponding author

Links

Table of Contents

Updates

  • [08/2019] Paper released on arXiv and accepted to ACM MM 2019.

Introduction

This is the official PyTorch implementation for the ACM MM 2019 paper Personalized Hashtag Recommendation for Micro-videos.

GCN_PHR is a novel graph convolutional networks (GCN) based personalized hashtag recommendation method tailored for micro-videos. It considers both users' preferences on post contents and their personal understanding of hashtags. By treating users, hashtags, and micro-videos as nodes in a graph, our model leverages message-passing strategies to learn their representations based on direct associations.

Highlights

  • Novel Graph-Based Modeling: Designs a GCN-based framework to explicitly model the complex interactions among <users, hashtags, micro-videos>.
  • Attention Mechanism: Employs an attention mechanism to filter redundant messages passed from micro-videos to users and hashtags, which significantly enhances representation learning.
  • Flexible Configurations: Supports multiple aggregation modes (mean, max, add) and scoring integration methods (cat, fully_con).
  • Comprehensive Baselines: Compares with established methods like UTM, ConTagNet, CSMN, and USHM.

Method / Framework

Framework

Project Structure

.
├── Data_Partition.py      # Script for data splitting
├── Dataset.py             # Data loading and processing script
├── model.py               # Core GCN_PHR model and architecture
├── train.py               # Training loop and evaluation logic
└── README.md

Installation

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

Dataset / Benchmark

Baselines:

Dataset

We provide two processed datasets: YFCC100M and Instagram.

  • You can find the full version of dataset via YFCC100M and raw data Instagram.
  • We select some users and micro-videos and extract the visual, acoustic, and textual features of micro-video.
#Micro-video #User #Hashtag Visual Acoustic Textual
YFCC100M 134,992 8,126 23,054 2,048 128 100
Instagram 48,888 2,303 12,194 2,048 128 100

Usage

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

  • YFCC100M dataset
    python train.py --l_r=0.001 --weight_decay=0.1 --batch_size=1024 --dim_latent=64 --num_workers=30 --aggr_mode='mean' --scoring_mode='cat'
  • Instagram dataset
    python train.py --l_r=0.001 --weight_decay=0.1 --batch_size=1024 --dim_latent=64 --num_workers=30 --aggr_mode='mean' --scoring_mode='cat'

Some important arguments:

  • 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'
  • scoring_mode:
    It indicates the implementation of user-specific micro-video/hashtag representation. Here we provide two options:
    1. cat(by default) concatenates the representation of user and micro-video/hashtag, and then feed into a MLP to calculate the representations. Usage --scoring_mode 'cat'
    2. fully_con calculates the user-specific micro-video/hashtag representations with a fully connected layer. Usage --concat 'fully_con'

-train.npy
Train file. Each line is a user with her/his hashtag towards the micro-video: (userID, Hashtag ID and micro-video ID)
-val.npy
Validation file. Each line is a user with her/his 1,000 negative hashtags and several positive hashtags for a micro-video: (userID, Neg_Hashtag ID, Pos_Hashtag ID, and micro-video ID)
-test.npy
Test file. Each line is a user with her/his 1,000 negative hashtags and several positive hashtags for a micro-video: (userID, Neg_Hashtag ID, Pos_Hashtag ID, and micro-video ID)

License

Copyright (C) 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.