Handling Information Loss of Graph Neural Networks for Session-based Recommendation

By Tianwen Chen, Raymond Chi-Wing Wong. Knowledge Discovery and Data Mining, 2020

306 citations recorded by the source corpus.

Recently, graph neural networks (GNNs) have gained increasing popularity due to their convincing performance in various applications. Many previous studies also attempted to apply GNNs to session-based recommendation and obtained promising results. However, we spot that there are two information loss problems in these GNN-based methods for session-based recommendation, namely the lossy session encoding problem and the ineffective long-range dependency capturing problem. The first problem is the lossy session encoding problem. Some sequential information about item transitions is ignored because of the lossy encoding from sessions to graphs and the permutation-invariant aggregation during message passing. The second problem is the ineffective long-range dependency capturing problem. Some long-range dependencies within sessions cannot be captured due to the limited number of layers. To solve the first problem, we propose a lossless encoding scheme and an edge-order preserving aggregation layer based on GRU that is dedicatedly designed to process the losslessly encoded graphs. To solve the second problem, we propose a shortcut graph attention layer that effectively captures long-range dependencies by propagating information along shortcut connections. By combining the two kinds of layers, we are able to build a model that does not have the information loss problems and outperforms the state-of-the-art models on three public datasets.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/b9631936ab9e41511f0eb85adb0fc7b8efb7983e

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. Hongzhi Yin
2. Junliang Yu
3. Xin Xia
4. Yong Li
5. Chen Gao
6. Li-zhen Cui
7. Xiangnan He
8. Depeng Jin

In Information Systems

1. Hongzhi Yin
2. Junliang Yu
3. Xin Xia
4. Yong Li
5. Chen Gao
6. Li-zhen Cui
7. Xiangnan He
8. Depeng Jin

Citations in the corpus

• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “Moreover, the methods with additional links, such as SGNN-HN [137], SHARE [173] and LESSR [24], attempt to”
• A Survey on Session-based Recommender Systems (2019, methodology): “…Graph ATtention (WGAT) network [89], another FGNN based on Broadly Connected Session graph to attentively exploit information both within and between sessions [88], and Shortcut Graph ATention (SGAT) to effectively propagate information along shortcut connections with attention mechanism [14].”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, unknown): “tion [203], [224], [225], [226], [227], [228], [255], [256].”
• Graph Neural Networks in Recommender Systems: A Survey (2020, methodology): “Due to the limited iteration of propagation, GNN cannot effectively capture long-range dependency among items [19].”
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, background): “2018) recently have drawn increasing attention and their applications in SBR also have shown promising results (Wang et al. 2020b,c; Yuan et al. 2019; Chen and Wong 2020).”
• Graph Neural Networks for Recommender System (2022, unknown): “based [5], cross-domain [10], multi-behavior [15], bundle recommendation [3], etc.”