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

Entire Space Cascade Delayed Feedback Modeling for Effective Conversion Rate Prediction