  The ubiquity of implicit feedback makes them the default choice to build
online recommender systems. While the large volume of implicit feedback
alleviates the data sparsity issue, the downside is that they are not as clean
in reflecting the actual satisfaction of users. For example, in E-commerce, a
large portion of clicks do not translate to purchases, and many purchases end
up with negative reviews. As such, it is of critical importance to account for
the inevitable noises in implicit feedback for recommender training. However,
little work on recommendation has taken the noisy nature of implicit feedback
into consideration.
  In this work, we explore the central theme of denoising implicit feedback for
recommender training. We find serious negative impacts of noisy implicit
feedback,i.e., fitting the noisy data prevents the recommender from learning
the actual user preference. Our target is to identify and prune noisy
interactions, so as to improve the quality of recommender training. By
observing the process of normal recommender training, we find that noisy
feedback typically has large loss values in the early stages. Inspired by this
observation, we propose a new training strategy namedAdaptive Denoising
Training(ADT), which adaptively prunes noisy interactions during training.
Specifically, we devise two paradigms for adaptive loss formulation: Truncated
Loss that discards the large-loss samples with a dynamic threshold in each
iteration; and reweighted Loss that adaptively lowers the weight of large-loss
samples. We instantiate the two paradigms on the widely used binary
cross-entropy loss and test the proposed ADT strategies on three representative
recommenders. Extensive experiments on three benchmarks demonstrate that ADT
significantly improves the quality of recommendation over normal training.
