Adversarial Heterogeneous Graph Neural Network for Robust Recommendation

Sang, L; Xu, M; Qian, S; Wu, X

Adversarial Heterogeneous Graph Neural Network for Robust Recommendation

Sang, L Xu, M

Qian, S Wu, X

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Social Systems, 2023, 10, (5), pp. 2660-2671
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Sang, L
dc.contributor.author	Xu, M https://orcid.org/0000-0001-9581-8849
dc.contributor.author	Qian, S
dc.contributor.author	Wu, X
dc.date.accessioned	2024-04-30T03:15:32Z
dc.date.available	2024-04-30T03:15:32Z
dc.date.issued	2023-10-01
dc.identifier.citation	IEEE Transactions on Computational Social Systems, 2023, 10, (5), pp. 2660-2671
dc.identifier.issn	2329-924X
dc.identifier.issn	2329-924X
dc.identifier.uri	http://hdl.handle.net/10453/178491
dc.description.abstract	Recommendation systems play a vital role in identifying the hidden interactions between users and items in online social networks. Recently, graph neural networks (GNNs) have exhibited significant performance gains by modeling the information propagation process in graph-structured data for a recommendation. However, existing GNN-based methods do not have broad applicability to heterogeneous graphs that integrate auxiliary data with diverse types. Moreover, graph structures are susceptible to noise and even unnoticed malicious perturbations, as perturbations from connected nodes can create cumulative effects on a target node in the graph. To enhance the robustness and generalization of GNN-based recommendations, we propose a new optimization model named Adversarial Heterogeneous Graph Neural Network for RECommendation (AHGNNRec). First, AHGNNRec learns user and item embeddings by exploring the distinct contributions of various types of interactions between users and items using a hierarchical heterogeneous graph neural network (HGNN). Second, to produce more robust embeddings for recommendations, we employ the adversarial training (AT) method to optimize the HGNN layers. AT is a min-max optimization training process where the generated adversarial fake nodes from normal nodes with intentional perturbations try to maximally deteriorate the recommendation performance. Following this, we learn about these adversarial user or item nodes by minimizing the impact of an additional regularization term for the recommendation. The experimental outcomes on two real-world benchmark datasets demonstrate the effectiveness of AHGNNRec.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation	http://purl.org/au-research/grants/arc/LP210100129
dc.relation.ispartof	IEEE Transactions on Computational Social Systems
dc.relation.isbasedon	10.1109/TCSS.2023.3268683
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Adversarial Heterogeneous Graph Neural Network for Robust Recommendation
dc.type	Journal Article
utslib.citation.volume	10
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-30T03:15:29Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	10
utslib.citation.issue	5

Abstract:

Recommendation systems play a vital role in identifying the hidden interactions between users and items in online social networks. Recently, graph neural networks (GNNs) have exhibited significant performance gains by modeling the information propagation process in graph-structured data for a recommendation. However, existing GNN-based methods do not have broad applicability to heterogeneous graphs that integrate auxiliary data with diverse types. Moreover, graph structures are susceptible to noise and even unnoticed malicious perturbations, as perturbations from connected nodes can create cumulative effects on a target node in the graph. To enhance the robustness and generalization of GNN-based recommendations, we propose a new optimization model named Adversarial Heterogeneous Graph Neural Network for RECommendation (AHGNNRec). First, AHGNNRec learns user and item embeddings by exploring the distinct contributions of various types of interactions between users and items using a hierarchical heterogeneous graph neural network (HGNN). Second, to produce more robust embeddings for recommendations, we employ the adversarial training (AT) method to optimize the HGNN layers. AT is a min-max optimization training process where the generated adversarial fake nodes from normal nodes with intentional perturbations try to maximally deteriorate the recommendation performance. Following this, we learn about these adversarial user or item nodes by minimizing the impact of an additional regularization term for the recommendation. The experimental outcomes on two real-world benchmark datasets demonstrate the effectiveness of AHGNNRec.

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http://hdl.handle.net/10453/178491