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PUID: A Personalized Deconfounding Framework for Recommender Systems under Hidden Confounding
Recommender systems often rely on observational user-item interaction data, which is prone to selection bias due to users' selective interactions with items. While techniques such as inverse propensity weighting (IPW) and doubly robust estimators are effective in addressing selection bias from obser
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