Collaborative filtering (CF) is a widely studied research topic in
recommender systems. The learning of a CF model generally depends on three
major components, namely interaction encoder, loss function, and negative
sampling. While many existing studies focus on the design of more powerful
interaction encoders, the impacts of loss functions and negative sampling
ratios have not yet been well explored. In this work, we show that the choice
of loss function as well as negative sampling ratio is equivalently important.
More specifically, we propose the cosine contrastive loss (CCL) and further
incorporate it to a simple unified CF model, dubbed SimpleX. Extensive
experiments have been conducted on 11 benchmark datasets and compared with 29
existing CF models in total. Surprisingly, the results show that, under our CCL
loss and a large negative sampling ratio, SimpleX can surpass most
sophisticated state-of-the-art models by a large margin (e.g., max 48.5%
improvement in NDCG@20 over LightGCN). We believe that SimpleX could not only
serve as a simple strong baseline to foster future research on CF, but also
shed light on the potential research direction towards improving loss function
and negative sampling.

通过对 11 个基准数据集进行广泛的实验，本研究体现了损失函数以及负采样比率的重要性， 并进一步将余弦对比损失函数用于一个简单的统一协同过滤模型 SimpleX 中， 结果表明，在 CCL 损失和大负采样比率下，SimpleX 可以大幅度超越大多数当前最先进的模型。