  The acquisition of explicit user feedback (e.g., ratings) in real-world
recommender systems is often hindered by the need for active user involvement.
To mitigate this issue, implicit feedback (e.g., clicks) generated during user
browsing is exploited as a viable substitute. However, implicit feedback
possesses a high degree of noise, which significantly undermines recommendation
quality. While many methods have been proposed to address this issue by
assigning varying weights to implicit feedback, two shortcomings persist: (1)
the weight calculation in these methods is iteration-independent, without
considering the influence of weights in previous iterations, and (2) the weight
calculation often relies on prior knowledge, which may not always be readily
available or universally applicable.
  To overcome these two limitations, we model recommendation denoising as a
bi-level optimization problem. The inner optimization aims to derive an
effective model for the recommendation, as well as guiding the weight
determination, thereby eliminating the need for prior knowledge. The outer
optimization leverages gradients of the inner optimization and adjusts the
weights in a manner considering the impact of previous weights. To efficiently
solve this bi-level optimization problem, we employ a weight generator to avoid
the storage of weights and a one-step gradient-matching-based loss to
significantly reduce computational time. The experimental results on three
benchmark datasets demonstrate that our proposed approach outperforms both
state-of-the-art general and denoising recommendation models. The code is
available at https://github.com/CoderWZW/BOD.
