The field of recommendation systems is moving towards more robust and accurate analysis of implicit feedback, which is critical for refining recommendations. Recent developments have focused on addressing the noise and biases inherent in implicit feedback, such as accidental clicks or exposure biases. Researchers are proposing innovative methods to denoise and interpret implicit feedback, including group-aware user behavior simulation, denoising fake interests, and causal negative sampling. These approaches have shown significant improvements in recommendation performance and out-of-distribution generalization. Noteworthy papers include G-UBS, which leverages contextual guidance from relevant user groups to robustly interpret implicit feedback, and CNSDiff, which synthesizes negative samples in the latent space to reduce bias and promote out-of-distribution generalization. Additionally, CrossDenoise offers a lightweight and entity-aware framework for denoising implicit feedback, demonstrating significant gains in recommendation accuracy.