The learning objective is integral to collaborative filtering systems, where
the Bayesian Personalized Ranking (BPR) loss is widely used for learning
informative backbones. However, BPR often experiences slow convergence and
suboptimal local optima, partially because it only considers one negative item
for each positive item, neglecting the potential impacts of other unobserved
items. To address this issue, the recently proposed Sampled Softmax
Cross-Entropy (SSM) compares one positive sample with multiple negative
samples, leading to better performance. Our comprehensive experiments confirm
that recommender systems consistently benefit from multiple negative samples
during training. Furthermore, we introduce a \underline{Sim}plified Sampled
Softmax \underline{C}ross-\underline{E}ntropy Loss (SimCE), which simplifies
the SSM using its upper bound. Our validation on 12 benchmark datasets, using
both MF and LightGCN backbones, shows that SimCE significantly outperforms both
BPR and SSM.

学习目标对协同过滤系统至关重要，贝叶斯个性化排名（BPR）损失广泛用于学习信息丰富的骨干。然而，BPR 往往收敛较慢且局部最优解不理想，部分原因是因为它仅考虑每个正样本一个负样本，忽视了其他未观察到项目的潜在影响。为了解决这个问题，最近提出的采样 Softmax 交叉熵（SSM）将一个正样本与多个负样本进行比较，从而提高了性能。我们的综合实验证实，推荐系统在训练过程中始终受益于多个负样本。此外，我们引入了一种简化的采样 Softmax 交叉熵损失（SimCE），该损失使用其上界简化了 SSM。我们在 12 个基准数据集上进行验证，使用 MF 和 LightGCN 骨干，结果显示 SimCE 在性能上明显优于 BPR 和 SSM。