USE: Dynamic User Modeling with Stateful Sequence Models

arXiv:2403.13344v1 Announce Type: cross
Abstract: User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings from behavioral data. Yet behavior-based user embedding learning faces the unique challenge of dynamic user modeling. As users continuously interact with the apps, user embeddings should be periodically updated to account for users' recent and long-term behavior patterns. Existing methods highly rely on stateless sequence models that lack memory of …

abstract advances arxiv behavior behavioral data challenge cs.ai cs.cl cs.hc cs.ir cs.lg cs.si data dynamic embedding embeddings engagement forecasting modeling personalized role services type user engagement