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.

本文提出了一种基于 InfoNCE loss function 的无偏差对比表示学习方法（uCTRL），以有效消除协作过滤模型中的流行度偏差问题，并通过四种基准数据集的实验验证，证明其在 Recall@20 和 NDCG@20 上的性能优于现有的无偏导向推荐模型，最高可达 12.22％和 16.33％的增益。