  Recurrent recommender systems have been successful in capturing the temporal
dynamics in users' activity trajectories. However, recurrent neural networks
(RNNs) are known to have difficulty learning long-term dependencies. As a
consequence, RNN-based recommender systems tend to overly focus on short-term
user interests. This is referred to as the recency bias, which could negatively
affect the long-term user experience as well as the health of the ecosystem. In
this paper, we introduce the recency dropout technique, a simple yet effective
data augmentation technique to alleviate the recency bias in recurrent
recommender systems. We demonstrate the effectiveness of recency dropout in
various experimental settings including a simulation study, offline
experiments, as well as live experiments on a large-scale industrial
recommendation platform.
