A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering

• URL: http://arxiv.org/abs/2004.14734v1
• Date: Thu, 30 Apr 2020 12:50:39 GMT
• Title: A Robust Hierarchical Graph Convolutional Network Model for Collaborative Filtering
• Authors: Shaowen Peng, Tsunenori Mine
• Abstract summary: Graph Convolutional Network (GCN) has achieved great success and has been applied in various fields including recommender systems. GCN still suffers from many issues such as training difficulties, over-smoothing, vulnerable to adversarial attacks, etc.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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