A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering

04/30/2020
by   Shaowen Peng, et al.
0

Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. However, GCN still suffers from many issues such as training difficulties, over-smoothing, vulnerable to adversarial attacks, etc. Distinct from current GCN-based methods which simply employ GCN for recommendation, in this paper we are committed to build a robust GCN model for collaborative filtering. Firstly, we argue that recursively incorporating messages from different order neighborhood mixes distinct node messages indistinguishably, which increases the training difficulty; instead we choose to separately aggregate different order neighbor messages with a simple GCN model which has been shown effective; then we accumulate them together in a hierarchical way without introducing additional model parameters. Secondly, we propose a solution to alleviate over-smoothing by randomly dropping out neighbor messages at each layer, which also well prevents over-fitting and enhances the robustness. Extensive experiments on three real-world datasets demonstrate the effectiveness and robustness of our model.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/05/2019

Binarized Collaborative Filtering with Distilling Graph Convolutional Networks

The efficiency of top-K item recommendation based on implicit feedback a...
research
04/24/2022

Less is More: Reweighting Important Spectral Graph Features for Recommendation

As much as Graph Convolutional Networks (GCNs) have shown tremendous suc...
research
04/08/2022

IA-GCN: Interactive Graph Convolutional Network for Recommendation

Recently, Graph Convolutional Network (GCN) has become a novel state-of-...
research
06/07/2023

Efficient Recruitment Strategy for Collaborative Mobile Crowd Sensing Based on GCN Trustworthiness Prediction

Collaborative Mobile Crowd Sensing (CMCS) enhances data quality and cove...
research
02/16/2023

Robust Mid-Pass Filtering Graph Convolutional Networks

Graph convolutional networks (GCNs) are currently the most promising par...
research
08/26/2022

SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation

With the tremendous success of Graph Convolutional Networks (GCNs), they...
research
06/07/2020

Single-Layer Graph Convolutional Networks For Recommendation

Graph Convolutional Networks (GCNs) and their variants have received sig...

Please sign up or login with your details

Forgot password? Click here to reset