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Recommender models aim to capture user preferences from historical feedbackand then predict user-specific feedback on candidate items. However, thepresence of various unmeasured confounders causes deviations between the userpreferences in the historical feedback and the true preferences, resulting inmodels not meeting their expected performance. Existing debias models either(1) specific to solving one particular bias or (2) directly obtain auxiliaryinformation from user historical feedback, which cannot identify whether thelearned preferences are true user preferences or mixed with unmeasuredconfounders. Moreover, we find that the former recommender system is not only asuccessor to unmeasured confounders but also acts as an unmeasured confounderaffecting user preference modeling, which has always been neglected in previousstudies. To this end, we incorporate the effect of the former recommendersystem and treat it as a proxy for all unmeasured confounders. We propose anovel framework, \textbf{S}eparating and \textbf{L}earning Latent Confounders\textbf{F}or \textbf{R}ecommendation (\textbf{SLFR}), which obtains therepresentation of unmeasured confounders to identify the counterfactualfeedback by disentangling user preferences and unmeasured confounders, thenguides the target model to capture the true preferences of users. Extensiveexperiments in five real-world datasets validate the advantages of our method.

Separating and Learning Latent Confounders to Enhancing User Preferences Modeling