  Because implicit user feedback for the collaborative filtering (CF) models is
biased toward popular items, CF models tend to yield recommendation lists with
popularity bias. Previous studies have utilized inverse propensity weighting
(IPW) or causal inference to mitigate this problem. However, they solely employ
pointwise or pairwise loss functions and neglect to adopt a contrastive loss
function for learning meaningful user and item representations. In this paper,
we propose Unbiased ConTrastive Representation Learning (uCTRL), optimizing
alignment and uniformity functions derived from the InfoNCE loss function for
CF models. Specifically, we formulate an unbiased alignment function used in
uCTRL. We also devise a novel IPW estimation method that removes the bias of
both users and items. Despite its simplicity, uCTRL equipped with existing CF
models consistently outperforms state-of-the-art unbiased recommender models,
up to 12.22% for Recall@20 and 16.33% for NDCG@20 gains, on four benchmark
datasets.
