  As its availability and generality in online services, implicit feedback is
more commonly used in recommender systems. However, implicit feedback usually
presents noisy samples in real-world recommendation scenarios (such as
misclicks or non-preferential behaviors), which will affect precise user
preference learning. To overcome the noisy samples problem, a popular solution
is based on dropping noisy samples in the model training phase, which follows
the observation that noisy samples have higher training losses than clean
samples. Despite the effectiveness, we argue that this solution still has
limits. (1) High training losses can result from model optimization instability
or hard samples, not just noisy samples. (2) Completely dropping of noisy
samples will aggravate the data sparsity, which lacks full data exploitation.
To tackle the above limitations, we propose a Double Correction Framework for
Denoising Recommendation (DCF), which contains two correction components from
views of more precise sample dropping and avoiding more sparse data. In the
sample dropping correction component, we use the loss value of the samples over
time to determine whether it is noise or not, increasing dropping stability.
Instead of averaging directly, we use the damping function to reduce the bias
effect of outliers. Furthermore, due to the higher variance exhibited by hard
samples, we derive a lower bound for the loss through concentration inequality
to identify and reuse hard samples. In progressive label correction, we
iteratively re-label highly deterministic noisy samples and retrain them to
further improve performance. Finally, extensive experimental results on three
datasets and four backbones demonstrate the effectiveness and generalization of
our proposed framework.
