A novel variation used in Reinforcement Learning to manage "enhanced sequential memory," helping stabilize deep learning agents.
Traditionally, CVR models were trained only on "clicked" samples. This created a massive , as the model only learned from a tiny subset of total impressions, leading to poor performance when faced with the "entire space" of all possible user-item interactions. How ESMM Works: The "Entire Space" Approach A novel variation used in Reinforcement Learning to
Predicted directly over the entire impression space. A novel variation used in Reinforcement Learning to
ESMM reimagines the problem by modeling the sequential pattern of user actions: Impression →right arrow →right arrow A novel variation used in Reinforcement Learning to