Global Context Enhanced Graph Neural Networks for Session-based Recommendation (2106.05081v1)

Abstract: 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.

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

The paper presents a novel approach to session-based recommendation systems with the introduction of Global Context Enhanced Graph Neural Networks (GCE-GNN). The proposed method addresses key challenges in session-based recommendation by enhancing traditional models with global contextual information derived from all sessions, alongside a refined local session-based analysis.

Overview of GCE-GNN

Session-based recommendation aims to predict the next item of interest to users from their current session, often without prior user data. Traditional models either focus on the session in isolation or struggle to integrate useful contextual data from related sessions beyond the immediate one. GCE-GNN introduces a framework that effectively utilizes both in-session data and global information from cumulative session histories to enhance predictive accuracy.

The architecture of GCE-GNN is built upon several integral components:

1. Session Graph and Global Graph: The model constructs two types of graphs for item embedding and transition pattern recognition. The session graph captures pairwise item interactions within the current session, while the global graph aggregates item transitions across all sessions. Such an approach allows the model to recognize both local and global inter-item dynamics.
2. Dual-Level Item Embeddings: GCE-GNN learns item representations at both the session level and the global level. Session-level embeddings capture immediate sequential dynamics, whereas global-level embeddings consider broader session interactions contextualized by a session-aware attention mechanism. This ensures a balanced embedding of the immediate session context and a holistic view derived from the global graph.
3. Attention and Aggregation: The model implements a session-aware attention mechanism for refining global contextual contributions. This approach allows GCE-GNN to aggregate insights from entire session histories with a focus on relevance and recency, providing a nuanced understanding of user preferences.
4. Improving Recommendation with Reversed Position Embedding: To further refine the session representation, the model incorporates position information inversely. This ensures that more recent user interactions receive greater weight, aligning recommendations closer to current user interests.

Performance Evaluation

The empirical analysis across three benchmark datasets—Diginetica, Tmall, and Nowplaying—demonstrates GCE-GNN's superiority over existing state-of-the-art baselines. The experiments show that GCE-GNN consistently outperforms methods such as SR-GNN and FGNN, particularly highlighting its ability to capture and utilize global transitions effectively. The introduction of the global context, along with sophisticated attention mechanisms, significantly enhances predictive performance.

Implications and Future Directions

The introduction of GCE-GNN marks a step forward in session-based recommendation by seamlessly integrating global contextualization with session-centric design. By leveraging the global graph, the model bypasses the limitations of isolated session analysis, offering a template for enhancing other recommendation tasks that might benefit from global pattern aggregation.

The adoption of graph neural networks for this task opens several future directions. Exploration into even deeper contextual signals, such as user demographic generalized representations, might provide further gains in understanding session behaviors. Additionally, the experimental setup can be expanded to include various domain-specific datasets to test the robustness and adaptability of GCE-GNN across different contexts.

In summary, by offering an innovative approach to session-based recommendation, GCE-GNN enhances the representational power of graph neural networks, incorporating both session-local and highly contextualized global data. This dual perspective methodology presents clear advantages, presenting a proficient framework for session-based recommendation that can be expanded and refined in continuously evolving recommender systems.