Self-Supervised Learning in Event Sequences: A Comparative Study and Hybrid Approach of Generative Modeling and Contrastive Learning
CoRR(2024)
摘要
This study investigates self-supervised learning techniques to obtain
representations of Event Sequences. It is a key modality in various
applications, including but not limited to banking, e-commerce, and healthcare.
We perform a comprehensive study of generative and contrastive approaches in
self-supervised learning, applying them both independently. We find that there
is no single supreme method. Consequently, we explore the potential benefits of
combining these approaches. To achieve this goal, we introduce a novel method
that aligns generative and contrastive embeddings as distinct modalities,
drawing inspiration from contemporary multimodal research.
Generative and contrastive approaches are often treated as mutually
exclusive, leaving a gap for their combined exploration. Our results
demonstrate that this aligned model performs at least on par with, and mostly
surpasses, existing methods and is more universal across a variety of tasks.
Furthermore, we demonstrate that self-supervised methods consistently
outperform the supervised approach on our datasets.
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