As the field progresses, several advanced versions of ESMM have been developed to address its inherent limitations:
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 As the field progresses, several advanced versions of
Predicted directly over the entire impression space. As the field progresses
ESMM reimagines the problem by modeling the sequential pattern of user actions: Impression →right arrow →right arrow As the field progresses, several advanced versions of
, the model can derive an unbiased CVR estimate even for items that haven't been clicked yet. Key Benefits of the ESMM Framework