Social Recommendation with Self-Supervised Metagraph Informax Network

Long, Xiaoling; Huang, Chao; Xu, Yong; Xu, Huance; Dai, Peng; Xia, Lianghao; Bo, Liefeng

doi:10.1145/3459637.3482480

Cited by 66 publications

(18 citation statements)

References 46 publications

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“…It performs dropout operations over the graph connection structures with different strategies, i.e., node dropout, edge dropout and random walk. SMIN [25] is a social-aware recommendation method with generative self-supervision. Inspired by the existing contrastive learning paradigms, this work proposes a new graph contrastive representation framework with the adaptive multi-behavior modeling, by exploring various semantic aspects of user-item interactions.…”

Section: Contrastive Representation Learningmentioning

confidence: 99%

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

Wei

Huang

Xia

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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131

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A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, social media). Traditional recommendation models usually assume that only a single type of interaction exists between user and item, and fail to model the multiplex user-item relationships from multi-typed user behavior data, such as page view, add-to-favourite and purchase. While some recent studies propose to capture the dependencies across different types of behaviors, two important challenges have been less explored: i) Dealing with the sparse supervision signal under target behaviors (e.g., purchase). ii) Capturing the personalized multi-behavior patterns with customized dependency modeling. To tackle the above challenges, we devise a new model CML, Contrastive Meta Learning (CML), to maintain dedicated crosstype behavior dependency for different users. In particular, we propose a multi-behavior contrastive learning framework to distill transferable knowledge across different types of behaviors via the constructed contrastive loss. In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users. Extensive experiments on three real-world datasets indicate that our method consistently outperforms various state-of-the-art recommendation methods. Our empirical studies further suggest that the contrastive meta learning paradigm offers great potential for capturing the behavior multiplicity in recommendation. We release our model implementation at: https://github.com/weiwei1206/CML.git. CCS CONCEPTS• Information systems → Recommender systems.

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Section: Contrastive Representation Learningmentioning

confidence: 99%

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

Wei

Huang

Xia

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

Self Cite

131

View full text Add to dashboard Cite

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“…It aims to learn quality discriminative representations by contrasting positive and negative samples from different views. Several recent attempts have brought the self-supervised learning to the recommendation [21,22,43,44]. For example, SGL [44] performs dropout operations over the graph connection structures with different strategies, i.e.,, node dropout, edge dropout and random walk.…”

Section: Related Workmentioning

confidence: 99%

Knowledge Graph Contrastive Learning for Recommendation

Yang,

Huang,

Xia

et al. 2022

Preprint

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Knowledge Graphs (KGs) have been utilized as useful side information to improve recommendation quality. In those recommender systems, knowledge graph information often contains fruitful facts and inherent semantic relatedness among items. However, the success of such methods relies on the high quality knowledge graphs, and may not learn quality representations with two challenges: i) The long-tail distribution of entities results in sparse supervision signals for KG-enhanced item representation; ii) Real-world knowledge graphs are often noisy and contain topic-irrelevant connections between items and entities. Such KG sparsity and noise make the item-entity dependent relations deviate from reflecting their true characteristics, which significantly amplifies the noise effect and hinders the accurate representation of user's preference.To fill this research gap, we design a general Knowledge Graph Contrastive Learning framework (KGCL) that alleviates the information noise for knowledge graph-enhanced recommender systems. Specifically, we propose a knowledge graph augmentation schema to suppress KG noise in information aggregation, and derive more robust knowledge-aware representations for items. In addition, we exploit additional supervision signals from the KG augmentation process to guide a cross-view contrastive learning paradigm, giving a greater role to unbiased user-item interactions in gradient descent and further suppressing the noise. Extensive experiments on three public datasets demonstrate the consistent superiority of our KGCL over state-of-the-art techniques. KGCL also achieves strong performance in recommendation scenarios with sparse user-item interactions, long-tail and noisy KG entities. Our implementation codes are available at https://github.com/yuh-yang/KGCL-SIGIR22. CCS CONCEPTS• Information systems → Recommender systems.

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“…Heterogeneous Graph Representation. Modeling the heterogeneous context of graphs has already received some attention [4,12,21,27,40]. For example, some studies leverage random walks to construct meta-paths over the heterogeneous graph for node embeddings, including metapath2vec [4] and HERec [27].…”

Section: Related Workmentioning

confidence: 99%

Multiplex Heterogeneous Graph Convolutional Network

et al. 2022

Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

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Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed nodes and different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding. Our MHGCN can automatically learn the useful heterogeneous metapath interactions of different lengths in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on five real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/NSSSJSS/MHGCN. CCS CONCEPTS• Mathematics of computing → Graph algorithms; • Computing methodologies → Learning latent representations.

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Social Recommendation with Self-Supervised Metagraph Informax Network

Cited by 66 publications

References 46 publications

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

Knowledge Graph Contrastive Learning for Recommendation

Multiplex Heterogeneous Graph Convolutional Network

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