Metapath- and Entity-aware Graph Neural Network for Recommendation

• URL: http://arxiv.org/abs/2010.11793v3
• Date: Fri, 2 Apr 2021 02:14:56 GMT
• Title: Metapath- and Entity-aware Graph Neural Network for Recommendation
• Authors: Muhammad Umer Anwaar, Zhiwei Han, Shyam Arumugaswamy, Rayyan Ahmad Khan, Thomas Weber, Tianming Qiu, Hao Shen, Yuanting Liu, Martin Kleinsteuber
• Abstract summary: In graph neural networks (GNNs) message passing iteratively aggregates nodes' information from their direct neighbors. Such sequential node connections e.g., metapaths, capture critical insights for downstream tasks. We employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs. PEAGNN trains multilayer GNNs to perform metapath-aware information aggregation on such subgraphs.
• Score: 10.583077434945187
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In graph neural networks (GNNs), message passing iteratively aggregates nodes' information from their direct neighbors while neglecting the sequential nature of multi-hop node connections. Such sequential node connections e.g., metapaths, capture critical insights for downstream tasks. Concretely, in recommender systems (RSs), disregarding these insights leads to inadequate distillation of collaborative signals. In this paper, we employ collaborative subgraphs (CSGs) and metapaths to form metapath-aware subgraphs, which explicitly capture sequential semantics in graph structures. We propose meta\textbf{P}ath and \textbf{E}ntity-\textbf{A}ware \textbf{G}raph \textbf{N}eural \textbf{N}etwork (PEAGNN), which trains multilayer GNNs to perform metapath-aware information aggregation on such subgraphs. This aggregated information from different metapaths is then fused using attention mechanism. Finally, PEAGNN gives us the representations for node and subgraph, which can be used to train MLP for predicting score for target user-item pairs. To leverage the local structure of CSGs, we present entity-awareness that acts as a contrastive regularizer on node embedding. Moreover, PEAGNN can be combined with prominent layers such as GAT, GCN and GraphSage. Our empirical evaluation shows that our proposed technique outperforms competitive baselines on several datasets for recommendation tasks. Further analysis demonstrates that PEAGNN also learns meaningful metapath combinations from a given set of metapaths.

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