  Implicit feedback is widely leveraged in recommender systems since it is easy
to collect and provides weak supervision signals. Recent works reveal a huge
gap between the implicit feedback and user-item relevance due to the fact that
implicit feedback is also closely related to the item exposure. To bridge this
gap, existing approaches explicitly model the exposure and propose unbiased
estimators to improve the relevance. Unfortunately, these unbiased estimators
suffer from the high gradient variance, especially for long-tail items, leading
to inaccurate gradient updates and degraded model performance. To tackle this
challenge, we propose a low-variance unbiased estimator from a probabilistic
perspective, which effectively bounds the variance of the gradient. Unlike
previous works which either estimate the exposure via heuristic-based
strategies or use a large biased training set, we propose to estimate the
exposure via an unbiased small-scale validation set. Specifically, we first
parameterize the user-item exposure by incorporating both user and item
information, and then construct an unbiased validation set from the biased
training set. By leveraging the unbiased validation set, we adopt bi-level
optimization to automatically update exposure-related parameters along with
recommendation model parameters during the learning. Experiments on two
real-world datasets and two semi-synthetic datasets verify the effectiveness of
our method.
