Global Context Enhanced Graph Neural Networks for Session-based Recommendation

By Ziyang Wang, Wei Wei, Gao Cong, Xiaoli Li, Xian-Ling Mao, Minghui Qiu. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020

626 citations recorded by the source corpus.

Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Almost all the existing solutions for SBR model user preference only based on the current session without exploiting the other sessions, which may contain both relevant and irrelevant item-transitions to the current session. This paper proposes a novel approach, called Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session. Specifically, GCE-GNN learns two levels of item embeddings from session graph and global graph, respectively: (i) Session graph, which is to learn the session-level item embedding by modeling pairwise item-transitions within the current session; and (ii) Global graph, which is to learn the global-level item embedding by modeling pairwise item-transitions over all sessions. In GCE-GNN, we propose a novel global-level item representation learning layer, which employs a session-aware attention mechanism to recursively incorporate the neighbors' embeddings of each node on the global graph. We also design a session-level item representation learning layer, which employs a GNN on the session graph to learn session-level item embeddings within the current session. Moreover, GCE-GNN aggregates the learnt item representations in the two levels with a soft attention mechanism. Experiments on three benchmark datasets demonstrate that GCE-GNN outperforms the state-of-the-art methods consistently.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b05a5424d0fce45896b6b8a847cf540a38f556bc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Hongzhi Yin
3. Xiang Wang
4. Junliang Yu
5. Xin Xia
6. Yong Li
7. Chen Gao
8. Meng Wang

In Information Systems

1. Xiangnan He
2. Hongzhi Yin
3. Xiang Wang
4. Junliang Yu
5. Xin Xia
6. Meng Wang
7. Yong Li
8. Chen Gao

Citations in the corpus

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “In session-based recommendation, most existing works [145, 187, 203, 215, 224] model the session data with a directed graph to capture the item transition pattern.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “tion [203], [224], [225], [226], [227], [228], [255], [256].”
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, methodology): “FGNN (Qiu et al. 2019) constructs a session graph to learn item transition pattern and rethinks the sequence order of items in SBR. GCE-GNN (Wang et al. 2020c) conduct graph convolution on both the single session graph and the global session graph to learn session-level and global-level embed-dings.”
• Graph Neural Networks in Recommender Systems: A Survey (2020, methodology): “GCE-GNN [163] and DAT-MDI [14] exploit the item transitions in all sessions to assist the transition patterns in the current sequence, which leverage the local context and global context.”
• Sequential Recommendation with Graph Neural Networks (2021, background): “Recently, graph-based models [21, 31, 33, 35] achieve successes on this task.”