Graph Meta Network for Multi-Behavior Recommendation

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

doi:10.1145/3404835.3462972

Cited by 146 publications

(66 citation statements)

References 37 publications

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“…Under the multi-typed user-item interactions, there exist some recent works attempting to designing effective approaches for handling behavior multiplicity [2,23,[50][51][52]. In particular, the behavior-wise relationships are characterized by attention mechanism in [50,51].…”

Section: Multi-behavior Recommender Systemsmentioning

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: Multi-behavior Recommender Systemsmentioning

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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“…Specifically, for each target user, we regard all his/her noninteracted items as negative samples to infer the preference of this user. For the performance evaluation, two representative metrics: Recall@N and NDCG@N are used to evaluate the accuracy of top-𝑁 recommended items [39,46]. Average evaluation results across all users in the test set are reported with 𝑁 = 20 by default.…”

Section: Evaluation Protocolsmentioning

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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“…To capture high-order collaborative signals, one prominent direction explores user-item relations based on multi-hop interaction topological structures with graph oriented approaches [16,35,41,44]. For example, NGCF [35] and PinSage [44] have demonstrated the importance of high-order connectivity between users and items for collaborative filtering.…”

Section: Graph-based Recommender Systemsmentioning

confidence: 99%

“…Some recent studies also follow the graph-structured information propagation rule to refine user/item embeddings, with various neighborhood aggregation functions [15]. For example, learning disentangled or behavior-aware user representations is proposed to improve CF paradigm, e.g., DGCF [36], MacridVAE [24] and MBGMN [41]. The hyperbolic embedding space is adopted to encode high-order information from neighboring users/items in [32].…”

Section: Graph-based Recommender Systemsmentioning

confidence: 99%

Hypergraph Contrastive Collaborative Filtering

Xia,

Huang,

et al. 2022

Preprint

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Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-theart performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraphenhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, so as to comprehensively capture the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. Our model implementation codes are available at https://github.com/akaxlh/HCCF.

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Graph Meta Network for Multi-Behavior Recommendation

Cited by 146 publications

References 37 publications

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

Contrastive Meta Learning with Behavior Multiplicity for Recommendation

Knowledge Graph Contrastive Learning for Recommendation

Hypergraph Contrastive Collaborative Filtering

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