Edge-Enhanced Global Disentangled Graph Neural Network for Sequential Recommendation
This addresses the need for better sequential recommendation systems by distinguishing item relationships, though it appears incremental as it builds on existing graph neural network and variational auto-encoder methods.
The paper tackles the problem of sequential recommendation by proposing an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) to capture item relationships, achieving crucial improvements over state-of-the-art baselines on three real-world datasets.
Sequential recommendation has been a widely popular topic of recommender systems. Existing works have contributed to enhancing the prediction ability of sequential recommendation systems based on various methods, such as recurrent networks and self-attention mechanisms. However, they fail to discover and distinguish various relationships between items, which could be underlying factors which motivate user behaviors. In this paper, we propose an Edge-Enhanced Global Disentangled Graph Neural Network (EGD-GNN) model to capture the relation information between items for global item representation and local user intention learning. At the global level, we build a global-link graph over all sequences to model item relationships. Then a channel-aware disentangled learning layer is designed to decompose edge information into different channels, which can be aggregated to represent the target item from its neighbors. At the local level, we apply a variational auto-encoder framework to learn user intention over the current sequence. We evaluate our proposed method on three real-world datasets. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines and is able to distinguish item features.