  The ubiquity of implicit feedback makes it indispensable for building
recommender systems. However, it does not actually reflect 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 importance to account for the inevitable noises in implicit
feedback. 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 learning, including
training and inference. 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
named Adaptive Denoising Training (ADT), which adaptively prunes the noisy
interactions by two paradigms (i.e., Truncated Loss and Reweighted Loss).
Furthermore, we consider extra feedback (e.g., rating) as auxiliary signal and
propose three strategies to incorporate extra feedback into ADT: finetuning,
warm-up training, and colliding inference. We instantiate the two paradigms on
the widely used binary cross-entropy loss and test them on three representative
recommender models. Extensive experiments on three benchmarks demonstrate that
ADT significantly improves the quality of recommendation over normal training
without using extra feedback. Besides, the proposed three strategies for using
extra feedback largely enhance the denoising ability of ADT.
