Diffusion-Based Graph Contrastive Learning for Recommendation with Implicit Feedback

Zhang, Lingzi; Liu, Yong; Zhou, Xin; Chen, Miao; Wang, Guoxin; Tang, Haitao

doi:10.1007/978-3-031-00126-0_15

Cited by 17 publications

(9 citation statements)

References 17 publications

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“…For example, GRU4Rec [Hidasi et al, 2016] treats users' behavior sequences as time series data and uses a multi-layer GRU structure to capture the sequential patterns. Moreover, some works, e.g., NARM [Li et al, 2017] and DREAM [Yu et al, 2016], combine attention mechanisms with GRU structures to learn users' dynamic representations. Simultaneously, Convolutional Neural Networks (CNN) have also been explored for sequential recommendation.…”

Section: Sequential Recommendationmentioning

confidence: 99%

“…For instance, SR-GNN converts sequences to graph structure data and employs the gated graph neural network to perform information propagation on the graph. GC-SAN [Xu et al, 2019] dynamically builds a graph for each sequence and models the local dependencies and long-range dependencies between items by combining GNN and self-attention mechanism. In addition, GCE-GNN builds the global graph and local graph to model global item transition patterns and local item transition patterns, respectively.…”

Section: Sequential Recommendationmentioning

confidence: 99%

“…• SR-GNN : This method converts sequences into graphs and leverages gated GNN layer to capture the item dependencies. • GC-SAN [Xu et al, 2019]: This method utilizes graph neural network and self-attention mechanism to dynamically capture rich local dependencies. • GCE-GNN : This method proposes to build global graph and local graph to model global transition patterns and local transition patterns, respectively.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…To remedy above issues, we first build a Weighted Item Transition Graph (WITG) to describe item transition patterns across the observed interaction sequences of all users. This transition graph can provide global context information for each user-item interaction [Xu et al, 2019]. To alleviate the impacts of data sparsity, neighborhood sampling on WITG is performed to build augmented graph views for each interaction sequence.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Sequential Recommendation with Graph Contrastive Learning

Zhang¹,

Liu²,

Xu³

et al. 2022

Preprint

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The sequential recommendation systems capture users' dynamic behavior patterns to predict their next interaction behaviors. Most existing sequential recommendation methods only exploit the local context information of an individual interaction sequence and learn model parameters solely based on the item prediction loss. Thus, they usually fail to learn appropriate sequence representations. This paper proposes a novel recommendation framework, namely Graph Contrastive Learning for Sequential Recommendation (GCL4SR). Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data. Moreover, GCL4SR uses subgraphs of WITG to augment the representation of each interaction sequence. Two auxiliary learning objectives have also been proposed to maximize the consistency between augmented representations induced by the same interaction sequence on WITG, and minimize the difference between the representations augmented by the global context on WITG and the local representation of the original sequence. Extensive experiments on real-world datasets demonstrate that GCL4SR consistently outperforms state-of-the-art sequential recommendation methods.

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Section: Sequential Recommendationmentioning

confidence: 99%

Section: Sequential Recommendationmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Sequential Recommendation with Graph Contrastive Learning

Zhang¹,

Liu²,

Xu³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…• GC-SAN [Xu et al, 2019]: This method utilizes graph neural network and self-attention mechanism to dynamically capture rich local dependencies.…”

Section: Experimental Settingsmentioning

confidence: 99%

Enhancing Sequential Recommendation with Graph Contrastive Learning

Zhou

Liu

Chen

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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Learning useful interactions between input features is crucial for tabular data modeling. Recent efforts start to explicitly model the feature interactions with graph, where each feature is treated as an individual node. However, the existing graph construction methods either heuristically formulate a fixed feature-interaction graph based on specific domain knowledge, or simply apply attention function to compute the pairwise feature similarities for each sample. While the fixed graph may be sub-optimal to downstream tasks, the sample-wise graph construction is time-consuming during model training and inference. To tackle these issues, we propose a framework named Table2Graph to transform the feature interaction modeling to learning a unified graph. Represented as a probability adjacency matrix, the unified graph learns to model the key feature interactions shared by the diverse samples in the tabular data. To well optimize the unified graph, we employ the reinforcement learning policy to capture the key feature interactions stably. A sparsity constraint is also proposed to regularize the learned graph from being overly-sparse/smooth. The experimental results in a variety of real-world applications demonstrate the effectiveness and efficiency of our Table2Graph, in terms of the prediction accuracy and feature interaction detection.

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