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. Our source code will be available at https://reczoo.github.io/SimpleX.

翻译：协同过滤（CF）是推荐系统中广泛研究的研究课题。CF模型的学习通常依赖于三个核心组件：交互编码器、损失函数和负采样。尽管现有研究多聚焦于设计更强大的交互编码器，但损失函数与负采样率的影响尚未得到充分探索。本文表明，损失函数的选择与负采样率具有同等重要性。具体而言，我们提出余弦对比损失（CCL），并将其整合至一个简单的统一CF模型SimpleX中。我们在11个基准数据集上开展广泛实验，并与共29个现有CF模型进行对比。令人惊讶的是，结果表明，在CCL损失与高负采样率的条件下，SimpleX能以显著优势超越大多数最先进的复杂模型（例如，在NDCG@20指标上较LightGCN最高提升48.5%）。我们认为SimpleX不仅能作为推动CF未来研究的简单强基线，更能为改进损失函数与负采样的潜在研究方向提供启示。我们的源代码将发布在https://reczoo.github.io/SimpleX。