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Recommender system is an essential part of online services, especially fore-commerce platform. Conversion Rate (CVR) prediction in RS plays a significantrole in optimizing Gross Merchandise Volume (GMV) goal of e-commerce. However,CVR suffers from well-known Sample Selection Bias (SSB) and Data Sparsity (DS)problems. Although existing methods ESMM and ESM2 train with all impressionsamples over the entire space by modeling user behavior paths, SSB and DSproblems still exist. In real practice, the online inference space are samplesfrom previous stage of RS process, rather than the impression space modeled byexisting methods. Moreover, existing methods solve the DS problem mainly bybuilding behavior paths of their own specific scene, ignoring the behaviors invarious scenes of e-commerce platform. In this paper, we propose Entire SpaceLearning Framework: Unbias Conversion Rate Prediction in Full Stages ofRecommender System, solving SSB and DS problems by reformulating GMV goal in anovel manner. Specifically, we rebuild the CVR on the entire data space withsamples from previous stage of RS process, unifying training and onlineinference space. Moreover, we explicitly introduce purchase samples from otherscenes of e-commerce platform in model learning process. Online A/B test andoffline experiments show the superiority of our framework. Our framework hasbeen deployed in rank stage of Taobao recommendation, providing recommendationservice for hundreds of millions of consumers everyday.

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