Recommender models aim to capture user preferences from historical feedback
and then predict user-specific feedback on candidate items. However, the
presence of various unmeasured confounders causes deviations between the user
preferences in the historical feedback and the true preferences, resulting in
models not meeting their expected performance. Existing debias models either
(1) specific to solving one particular bias or (2) directly obtain auxiliary
information from user historical feedback, which cannot identify whether the
learned preferences are true user preferences or mixed with unmeasured
confounders. Moreover, we find that the former recommender system is not only a
successor to unmeasured confounders but also acts as an unmeasured confounder
affecting user preference modeling, which has always been neglected in previous
studies. To this end, we incorporate the effect of the former recommender
system and treat it as a proxy for all unmeasured confounders. We propose a
novel framework, \textbf{S}eparating and \textbf{L}earning Latent Confounders
\textbf{F}or \textbf{R}ecommendation (\textbf{SLFR}), which obtains the
representation of unmeasured confounders to identify the counterfactual
feedback by disentangling user preferences and unmeasured confounders, then
guides the target model to capture the true preferences of users. Extensive
experiments in five real-world datasets validate the advantages of our method.

提出了一种名为 SLFR 的新框架，通过解开用户偏好和未测量混淆因素的联系，识别反事实反馈，以捕捉用户的真实偏好，并在五个真实世界数据集上进行了广泛的实验证明了该方法的优势。