AINeutralarXiv – CS AI · Mar 267/10
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Entire Space Counterfactual Learning for Reliable Content Recommendations
Researchers developed ESCM² (Entire Space Counterfactual Multitask Model), a new framework that improves post-click conversion rate estimation in recommender systems by addressing intrinsic estimation bias and false independence assumptions. The model-agnostic approach incorporates counterfactual learning to enhance recommendation accuracy and has been validated on large-scale industrial datasets.