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.

翻译：协同过滤是推荐系统中广泛研究的课题。协同过滤模型的学习通常依赖于三个主要组件：交互编码器、损失函数和负采样。尽管现有研究大多聚焦于设计更强大的交互编码器，但损失函数和负采样比例的影响尚未得到充分探索。本研究表明，损失函数的选择与负采样比例同样重要。具体而言，我们提出了余弦对比损失（CCL），并将其整合到一个名为SimpleX的简单统一协同过滤模型中。我们在11个基准数据集上进行了大量实验，并与总计29个现有协同过滤模型进行了比较。令人惊讶的是，结果表明，在CCL损失和大负采样比例下，SimpleX能够以显著优势超越大多数先进模型（例如，在NDCG@20指标上相较LightGCN最高提升48.5%）。我们相信，SimpleX不仅能作为促进未来协同过滤研究的简单强基线，也能为改进损失函数和负采样这一潜在研究方向提供启示。