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Collaborative filtering (CF) is a widely studied research topic inrecommender systems. The learning of a CF model generally depends on threemajor components, namely interaction encoder, loss function, and negativesampling. While many existing studies focus on the design of more powerfulinteraction encoders, the impacts of loss functions and negative samplingratios have not yet been well explored. In this work, we show that the choiceof loss function as well as negative sampling ratio is equivalently important.More specifically, we propose the cosine contrastive loss (CCL) and furtherincorporate it to a simple unified CF model, dubbed SimpleX. Extensiveexperiments have been conducted on 11 benchmark datasets and compared with 29existing CF models in total. Surprisingly, the results show that, under our CCLloss and a large negative sampling ratio, SimpleX can surpass mostsophisticated 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 onlyserve as a simple strong baseline to foster future research on CF, but alsoshed light on the potential research direction towards improving loss functionand negative sampling. Our source code will be available athttps://reczoo.github.io/SimpleX.

SimpleX: A Simple and Strong Baseline for Collaborative Filtering