[2604.09085] Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models

Computer Science > Machine Learning arXiv:2604.09085 (cs) [Submitted on 10 Apr 2026] Title:Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models Authors:Harry Proshian, Nikita Severin, Sergey Nikolenko, Kireev Ivan, Andrey Savchenko, Ivan Sergeev, Maria Postnova, Ilya Makarov View a PDF of the paper titled Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models, by Harry Proshian and 7 other authors View PDF HTML (experimental) 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. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2604.09085 [cs.LG]   (or arXiv:2604.09085v1 [cs.LG] for this...