Tripartite Heterogeneous Graph Propagation for Large-scale Social Recommendation

Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social graphs hinder the practical usage of GNNs for social recommendation, such as their complex noisy connections and high heter...

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Bibliographic Details
Published inarXiv.org
Main Authors Kyung-Min, Kim, Kwak, Donghyun, Kwak, Hanock, Young-Jin, Park, Sim, Sangkwon, Jae-Han, Cho, Kim, Minkyu, Kwon, Jihun, Nako Sung, Jung-Woo, Ha
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 24.07.2019
Subjects
Complexity
Graph neural networks
Graph theory
Graphical representations
Nodes
Propagation
Recommender systems
Search engines
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Summary:Graph Neural Networks (GNNs) have been emerging as a promising method for relational representation including recommender systems. However, various challenging issues of social graphs hinder the practical usage of GNNs for social recommendation, such as their complex noisy connections and high heterogeneity. The oversmoothing of GNNs is an obstacle of GNN-based social recommendation as well. Here we propose a new graph embedding method Heterogeneous Graph Propagation (HGP) to tackle these issues. HGP uses a group-user-item tripartite graph as input to reduce the number of edges and the complexity of paths in a social graph. To solve the oversmoothing issue, HGP embeds nodes under a personalized PageRank based propagation scheme, separately for group-user graph and user-item graph. Node embeddings from each graph are integrated using an attention mechanism. We evaluate our HGP on a large-scale real-world dataset consisting of 1,645,279 nodes and 4,711,208 edges. The experimental results show that HGP outperforms several baselines in terms of AUC and F1-score metrics.
ISSN:2331-8422