AI RESEARCH
Entire Space Counterfactual Learning for Reliable Content Recommendations
arXiv CS.LG
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ArXi:2210.11039v3 Announce Type: replace Post-click conversion rate (CVR) estimation is a fundamental task in developing effective recommender systems, yet it faces challenges from data sparsity and sample selection bias. To handle both challenges, the entire space multitask models are employed to decompose the user behavior track into a sequence of exposure $\rightarrow$ click $\rightarrow$ conversion, constructing surrogate learning tasks for CVR estimation.