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.

翻译：由于协同过滤（CF）模型对隐式用户反馈存在流行度偏倚，导致推荐列表常呈现流行度偏见。先前研究采用逆倾向加权（IPW）或因果推断来缓解该问题，但仅使用逐点或逐对损失函数，未采用对比损失函数学习有意义的用户和物品表征。本文提出无偏对比表示学习（uCTRL），通过优化源自InfoNCE损失函数的对齐性与均匀性函数，专用于CF模型。具体而言，我们设计了uCTRL中的无偏对齐函数，并提出一种新型IPW估计方法，可同时消除用户和物品的偏倚。尽管方法简洁，将uCTRL集成至现有CF模型后，其在四个基准数据集上持续优于最先进的无偏推荐模型，Recall@20最高提升12.22%，NDCG@20最高提升16.33%。