ESCM^2: Entire Space Counterfactual Multi-Task Model for Post-Click Conversion Rate Estimation

by   Hao Wang, et al.

Accurate estimation of post-click conversion rate is critical for building recommender systems, which has long been confronted with sample selection bias and data sparsity issues. Methods in the Entire Space Multi-task Model (ESMM) family leverage the sequential pattern of user actions, i.e. impression→ click → conversion to address data sparsity issue. However, they still fail to ensure the unbiasedness of CVR estimates. In this paper, we theoretically demonstrate that ESMM suffers from the following two problems: (1) Inherent Estimation Bias (IEB), where the estimated CVR of ESMM is inherently higher than the ground truth; (2) Potential Independence Priority (PIP) for CTCVR estimation, where there is a risk that the ESMM overlooks the causality from click to conversion. To this end, we devise a principled approach named Entire Space Counterfactual Multi-task Modelling (ESCM^2), which employs a counterfactual risk miminizer as a regularizer in ESMM to address both IEB and PIP issues simultaneously. Extensive experiments on offline datasets and online environments demonstrate that our proposed ESCM^2 can largely mitigate the inherent IEB and PIP issues and achieve better performance than baseline models.


page 1

page 2

page 3

page 4


Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications

As a basic research problem for building effective recommender systems, ...

ESMC: Entire Space Multi-Task Model for Post-Click Conversion Rate via Parameter Constraint

Large-scale online recommender system spreads all over the Internet bein...

DCMT: A Direct Entire-Space Causal Multi-Task Framework for Post-Click Conversion Estimation

In recommendation scenarios, there are two long-standing challenges, i.e...

A Causal Perspective to Unbiased Conversion Rate Estimation on Data Missing Not at Random

In modern e-commerce and advertising recommender systems, ongoing resear...

Modeling Orders of User Behaviors via Differentiable Sorting: A Multi-task Framework to Predicting User Post-click Conversion

User post-click conversion prediction is of high interest to researchers...

Conversion Rate Prediction via Post-Click Behaviour Modeling

Effective and efficient recommendation is crucial for modern e-commerce ...

Entire Space Learning Framework: Unbias Conversion Rate Prediction in Full Stages of Recommender System

Recommender system is an essential part of online services, especially f...

Please sign up or login with your details

Forgot password? Click here to reset