  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.
