Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models

• URL: http://arxiv.org/abs/2604.09085v1
• Date: Fri, 10 Apr 2026 08:11:41 GMT
• Title: Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models
• Abstract summary: 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.<n>We propose three model-agnostic strategies for integrating this structural information into contrastive SSL.<n>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.
• Score: 3.4975653064710186
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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