DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation

Yang, Liqi; Luo, Linhan; Xin, Lifeng; Zhang, Xiaofeng; Zhang, Xinni

doi:10.48550/arxiv.2105.14428

Cited by 2 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model CoHHN proposed by Zhang et al (2022) introduces price information. Yang et al (2021) proposed a demandbased graphical neural network (DAGNN). In particular, a requirements modeling component is proposed, which first extracts the session requirements and then uses the global requirements matrix to estimate the multiple basic requirements for each session.…”

Section: Session-based Recommendationmentioning

confidence: 99%

Multi-relation global context learning for session-based recommendation

Cao

2023

DTA

View full text Add to dashboard Cite

PurposeSession-based recommendation aims to predict the user's next preference based on the user's recent activities. Although most existing studies consider the global characteristics of items, they only learn the global characteristics of items based on a single connection relationship, which cannot fully capture the complex transformation relationship between items. We believe that multiple relationships between items in learning sessions can improve the performance of session recommendation tasks and the scalability of recommendation models. At the same time, high-quality global features of the item help to explore the potential common preferences of users.Design/methodology/approachThis work proposes a session-based recommendation method with a multi-relation global context–enhanced network to capture this global transition relationship. Specifically, we construct a multi-relation global item graph based on a group of sessions, use a graded attention mechanism to learn different types of connection relations independently and obtain the global feature of the item according to the multi-relation weight.FindingsWe did related experiments on three benchmark datasets. The experimental results show that our proposed model is superior to the existing state-of-the-art methods, which verifies the effectiveness of our model.Originality/valueFirst, we construct a multi-relation global item graph to learn the complex transition relations of the global context of the item and effectively mine the potential association of items between different sessions. Second, our model effectively improves the scalability of the model by obtaining high-quality item global features and enables some previously unconsidered items to make it onto the candidate list.

show abstract

Section: Session-based Recommendationmentioning

confidence: 99%

Multi-relation global context learning for session-based recommendation

Cao

2023

DTA

View full text Add to dashboard Cite

show abstract

“…For our evaluation on Cora, Citeseer, Pubmed, and OGBN-ArXiv, we have closely followed the data split settings and metrics reported by the recent benchmark [49]. See details in Appendix C. For comparison with SOTA models, we have used JKNet [50], InceptionGCN [51], SGC [52], GAT [3], GCNII [24], and DAGNN [53]. We use Adam optimizer for our experiments and performed a grid search to tune hyperparameters for our proposed methods and reported our settings in Table 1.…”

Section: Dataset and Experimental Setupmentioning

confidence: 99%

Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again

Jaiswal¹,

Peihao²,

Chen³

et al. 2022

Preprint

View full text Add to dashboard Cite

Despite the enormous success of Graph Convolutional Networks (GCNs) in modeling graph-structured data, most of the current GCNs are shallow due to the notoriously challenging problems of over-smoothening and information squashing along with conventional difficulty caused by vanishing gradients and over-fitting. Previous works have been primarily focused on the study of over-smoothening and over-squashing phenomena in training deep GCNs. Surprisingly, in comparison with CNNs/RNNs, very limited attention has been given to understanding how healthy gradient flow can benefit the trainability of deep GCNs. In this paper, firstly, we provide a new perspective of gradient flow to understand the substandard performance of deep GCNs and hypothesize that by facilitating healthy gradient flow, we can significantly improve their trainability, as well as achieve state-of-the-art (SOTA) level performance from vanilla-GCNs [1]. Next, we argue that blindly adopting the Glorot initialization for GCNs is not optimal, and derive a topologyaware isometric initialization scheme for vanilla-GCNs based on the principles of isometry. Additionally, contrary to ad-hoc addition of skip-connections, we propose to use gradient-guided dynamic rewiring of vanilla-GCNs with skip connections. Our dynamic rewiring method uses the gradient flow within each layer during training to introduce on-demand skip-connections adaptively. We provide extensive empirical evidence across multiple datasets that our methods improve gradient flow in deep vanilla-GCNs and significantly boost their performance to comfortably compete and outperform many fancy state-of-the-art methods. Codes are available at: https://github.com/VITA-Group/GradientGCN.

show abstract

DAGNN: Demand-aware Graph Neural Networks for Session-based Recommendation

Cited by 2 publications

References 33 publications

Multi-relation global context learning for session-based recommendation

Multi-relation global context learning for session-based recommendation

Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again

Contact Info

Product

Resources

About