  Recently sequential recommendations and next-item prediction task has become
increasingly popular in the field of recommender systems. Currently, two
state-of-the-art baselines are Transformer-based models SASRec and BERT4Rec.
Over the past few years, there have been quite a few publications comparing
these two algorithms and proposing new state-of-the-art models. In most of the
publications, BERT4Rec achieves better performance than SASRec. But BERT4Rec
uses cross-entropy over softmax for all items, while SASRec uses negative
sampling and calculates binary cross-entropy loss for one positive and one
negative item. In our work, we show that if both models are trained with the
same loss, which is used by BERT4Rec, then SASRec will significantly outperform
BERT4Rec both in terms of quality and training speed. In addition, we show that
SASRec could be effectively trained with negative sampling and still outperform
BERT4Rec, but the number of negative examples should be much larger than one.
