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The interaction data used by recommender systems (RSs) inevitably includenoises resulting from mistaken or exploratory clicks, especially under implicitfeedbacks. Without proper denoising, RS models cannot effectively captureusers' intrinsic preferences and the true interactions between users and items.To address such noises, existing methods mostly rely on auxiliary data whichare not always available. In this work, we ground on Optimal Transport (OT) toglobally match a user embedding space and an item embedding space, allowingboth non-deep and deep RS models to discriminate intrinsic and noisyinteractions without supervision. Specifically, we firstly leverage the OTframework via Sinkhorn distance to compute the continuous many-to-manyuser-item matching scores. Then, we relax the regularization in Sinkhorndistance to achieve a closed-form solution with a reduced time complexity.Finally, to consider individual user behaviors for denoising, we develop apartial OT framework to adaptively relabel user-item interactions through apersonalized thresholding mechanism. Extensive experiments show that ourframework can significantly boost the performances of existing RS models.

Partial Relaxed Optimal Transport for Denoised Recommendation