Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models
Summary: arXiv:2604.09085v1 Announce Type: cross
Abstract: Large-scale digital platforms generate billions of timestamped user-item interactions (events) that are crucial for predicting user attributes in, e.g., fraud prevention and recommendations. While self-supervised learning (SSL) effectively models the temporal order of events, it typically overlooks the global structure of the user-item interaction graph. To bridge this gap, we propose three model-agnostic strategies for integrating this structural information into contrastive SSL: enriching event embeddings, aligning client representations with graph embeddings, and adding a structural pretext task. Experiments on four financial and e-commerce datasets demonstrate that our approach consistently improves the accuracy (up to a 2.3% AUC) and reveals that graph density is a key factor in selecting the optimal integration strategy.
Introduction
The rise of digital platforms has led to an overwhelming amount of user-item interaction data. This data, characterized by timestamped events, serves as a critical resource for various applications, including fraud detection and personalized recommendations. The challenge lies in effectively modeling these interactions to derive meaningful insights.
Current Limitations
Self-supervised learning has emerged as a powerful technique for capturing the temporal dynamics of user interactions. However, existing methodologies often fail to take into account the broader structure of the interaction graph, which can result in missed opportunities for enhancing predictive accuracy.
Proposed Solutions
To address the limitations of current models, we propose three innovative strategies that can be seamlessly integrated into existing contrastive SSL frameworks:
- Enriching Event Embeddings: By enhancing the representation of events through additional contextual information, we can capture deeper insights into user behaviors.
- Aligning Client Representations with Graph Embeddings: This approach leverages the structural characteristics of the interaction graph to ensure that user representations are more aligned with their respective items.
- Adding a Structural Pretext Task: Incorporating a pretext task that focuses on the structure of the graph allows the model to learn more robust representations that reflect both temporal and structural dynamics.
Empirical Results
We conducted extensive experiments across four diverse datasets in the financial and e-commerce sectors. The results were promising, showcasing consistent improvements in accuracy metrics, with an increase of up to 2.3% in Area Under the Curve (AUC). Furthermore, analysis revealed that the density of the graph plays a crucial role in determining the most effective integration strategy.
Conclusion
Our findings underscore the importance of integrating graph-based embeddings into event sequence models, particularly in the context of self-supervised learning. As digital platforms continue to evolve, leveraging the structural aspects of user-item interactions can significantly enhance predictive capabilities, paving the way for more accurate fraud prevention and personalized recommendation systems.
In summary, this research not only contributes to the existing body of knowledge in the field of machine learning but also provides actionable insights for practitioners aiming to optimize user engagement through data-driven methodologies.