Collaborative filtering (CF) is a pivotal technique in modern recommender
systems. The learning process of CF models typically consists of three
components: interaction encoder, loss function, and negative sampling. Although
many existing studies have proposed various CF models to design sophisticated
interaction encoders, recent work shows that simply reformulating the loss
functions can achieve significant performance gains. This paper delves into
analyzing the relationship among existing loss functions. Our mathematical
analysis reveals that the previous loss functions can be interpreted as
alignment and uniformity functions: (i) the alignment matches user and item
representations, and (ii) the uniformity disperses user and item distributions.
Inspired by this analysis, we propose a novel loss function that improves the
design of alignment and uniformity considering the unique patterns of datasets
called Margin-aware Alignment and Weighted Uniformity (MAWU). The key novelty
of MAWU is two-fold: (i) margin-aware alignment (MA) mitigates
user/item-specific popularity biases, and (ii) weighted uniformity (WU) adjusts
the significance between user and item uniformities to reflect the inherent
characteristics of datasets. Extensive experimental results show that MF and
LightGCN equipped with MAWU are comparable or superior to state-of-the-art CF
models with various loss functions on three public datasets.

本文分析了现有损失函数之间的关系，揭示了先前的损失函数可以解释为对齐和均匀度函数，并基于此分析提出了一种新的损失函数 MAWU，该函数考虑了数据集的独特模式，通过缓解用户 / 物品特定的流行度偏差和调整用户和物品均匀度之间的重要性，使得使用 MAWU 的 MF 和 LightGCN 在三个公共数据集上与各种损失函数的最先进的协同过滤模型相媲美或更优。