Abacus: A Novel Self-Supervised Approach to Sequential User Modeling

This research introduces a novel self-supervised learning technique for sequential user modeling, potentially improving the accuracy of predictions based on user behavior. The paper's focus on distributional pretraining and event counting alignment suggests a sophisticated approach to capturing user patterns.

• •Proposes a self-supervised learning method for sequential user modeling.
• •Employs distributional pretraining and event counting alignment.
• •Aims to improve accuracy in predicting user behavior.