Entire Space Multi-Task Model ESSM
Tags: #machine learning #multi taskEquation
$$L(\theta_{cvr},\theta_{ctr})=\sum^{N}_{i=1}l(y_{i},f(x_{i};\theta_{ctr}))+\sum^{N}_{i=1}l(y_{i}\&z_{i},f(x_{i};\theta_{ctr}) \times f(x_{i};\theta_{cvr})) $$Latex Code
L(\theta_{cvr},\theta_{ctr})=\sum^{N}_{i=1}l(y_{i},f(x_{i};\theta_{ctr}))+\sum^{N}_{i=1}l(y_{i}\&z_{i},f(x_{i};\theta_{ctr}) \times f(x_{i};\theta_{cvr}))
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Introduction
Equation
Latex Code
L(\theta_{cvr},\theta_{ctr})=\sum^{N}_{i=1}l(y_{i},f(x_{i};\theta_{ctr}))+\sum^{N}_{i=1}l(y_{i}\&z_{i},f(x_{i};\theta_{ctr}) \times f(x_{i};\theta_{cvr}))
Explanation
ESSM model uses two separate towers to model pCTR prediction task and pCTCVR prediction task simultaneously.
Related Documents
- See below link of paper Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate for more details.
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