  Conversion rate (CVR) prediction is an essential task for large-scale
e-commerce platforms. However, refund behaviors frequently occur after
conversion in online shopping systems, which drives us to pay attention to
effective conversion for building healthier shopping services. This paper
defines the probability of item purchasing without any subsequent refund as an
effective conversion rate (ECVR). A simple paradigm for ECVR prediction is to
decompose it into two sub-tasks: CVR prediction and post-conversion refund rate
(RFR) prediction. However, RFR prediction suffers from data sparsity (DS) and
sample selection bias (SSB) issues, as the refund behaviors are only available
after user purchase. Furthermore, there is delayed feedback in both conversion
and refund events and they are sequentially dependent, named cascade delayed
feedback (CDF), which significantly harms data freshness for model training.
Previous studies mainly focus on tackling DS and SSB or delayed feedback for a
single event. To jointly tackle these issues in ECVR prediction, we propose an
Entire space CAscade Delayed feedback modeling (ECAD) method. Specifically,
ECAD deals with DS and SSB by constructing two tasks including CVR prediction
and conversion \& refund rate (CVRFR) prediction using the entire space
modeling framework. In addition, it carefully schedules auxiliary tasks to
leverage both conversion and refund time within data to alleviate CDF.
Experimental results on the offline industrial dataset and online A/B testing
demonstrate the effectiveness of ECAD. In addition, ECAD has been deployed in
one of the recommender systems in Alibaba, contributing to a significant
improvement of ECVR.
