Conversion rate (CVR) estimation aims to predict the probability of
conversion event after a user has clicked an ad. Typically, online publisher
has user browsing interests and click feedbacks, while demand-side advertising
platform collects users' post-click behaviors such as dwell time and conversion
decisions. To estimate CVR accurately and protect data privacy better, vertical
federated learning (vFL) is a natural solution to combine two sides' advantages
for training models, without exchanging raw data. Both CVR estimation and
applied vFL algorithms have attracted increasing research attentions. However,
standardized and systematical evaluations are missing: due to the lack of
standardized datasets, existing studies adopt public datasets to simulate a vFL
setting via hand-crafted feature partition, which brings challenges to fair
comparison. We introduce FedAds, the first benchmark for CVR estimation with
vFL, to facilitate standardized and systematical evaluations for vFL
algorithms. It contains a large-scale real world dataset collected from
Alibaba's advertising platform, as well as systematical evaluations for both
effectiveness and privacy aspects of various vFL algorithms. Besides, we also
explore to incorporate unaligned data in vFL to improve effectiveness, and
develop perturbation operations to protect privacy well. We hope that future
research work in vFL and CVR estimation benefits from the FedAds benchmark.

本研究介绍了 FedAds 基准测试，旨在为 CVR 估计中的基于垂直联邦学习的隐私保护提供标准化和系统化的评估，并探讨如何提高有效性和保护隐私。

使用竖直联邦学习进行隐私保护的 CVR 估计基准：FedAds

FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical  Federated Learning

As a basic research problem for building effective recommender systems,
post-click conversion rate (CVR) estimation has long been plagued by sample
selection bias and data sparsity issues. To address the data sparsity issue,
prevalent methods based on entire space multi-task model leverage the
sequential pattern of user actions, i.e. exposure $\rightarrow$ click
$\rightarrow$ conversion to construct auxiliary learning tasks. However, they
still fall short of guaranteeing the unbiasedness of CVR estimates. This paper
theoretically demonstrates two defects of these entire space multi-task models:
(1) inherent estimation bias (IEB) for CVR estimation, where the CVR estimate
is inherently higher than the ground truth; (2) potential independence priority
(PIP) for CTCVR estimation, where the causality from click to conversion might
be overlooked. This paper further proposes a principled method named entire
space counterfactual multi-task model (ESCM$^2$), which employs a
counterfactual risk minimizer to handle both IEB and PIP issues at once. To
demonstrate the effectiveness of the proposed method, this paper explores its
parameter tuning in practice, derives its analytic properties, and showcases
its effectiveness in industrial CVR estimation, where ESCM$^2$ can effectively
alleviate the intrinsic IEB and PIP issues and outperform baseline models.

针对构建有效的推荐系统的基本研究问题，本文提出了一个称为整个空间反事实多任务模型（ESCM2）的原则方法，该方法使用反事实风险最小化器一次处理两个问题以处理数据稀疏问题和保证 CVR 估计的无偏性。该方法证明了 ESCM2 优于基线模型。