Self-Supervised Learning in Event Sequences: A Comparative Study and Hybrid Approach of Generative Modeling and Contrastive Learning. (arXiv:2401.15935v2 [cs.LG] UPDATED)

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 …

applications arxiv banking commerce cs.lg e-commerce event generative generative modeling healthcare hybrid hybrid approach key modeling self-supervised learning study supervised learning