Increasing research interests focus on sequential recommender systems, aiming
to model dynamic sequence representation precisely. However, the most commonly
used loss function in state-of-the-art sequential recommendation models has
essential limitations. To name a few, Bayesian Personalized Ranking (BPR) loss
suffers the vanishing gradient problem from numerous negative sampling and
predictionbiases; Binary Cross-Entropy (BCE) loss subjects to negative sampling
numbers, thereby it is likely to ignore valuable negative examples and reduce
the training efficiency; Cross-Entropy (CE) loss only focuses on the last
timestamp of the training sequence, which causes low utilization of sequence
information and results in inferior user sequence representation. To avoid
these limitations, in this paper, we propose to calculate Cumulative
Cross-Entropy (CCE) loss over the sequence. CCE is simple and direct, which
enjoys the virtues of painless deployment, no negative sampling, and effective
and efficient training. We conduct extensive experiments on five benchmark
datasets to demonstrate the effectiveness and efficiency of CCE. The results
show that employing CCE loss on three state-of-the-art models GRU4Rec, SASRec,
and S3-Rec can reach 125.63%, 69.90%, and 33.24% average improvement of full
ranking NDCG@5, respectively. Using CCE, the performance curve of the models on
the test data increases rapidly with the wall clock time, and is superior to
that of other loss functions in almost the whole process of model training.

本文提出了一种基于序列的 Cumulative Cross-Entropy (CCE) 损失函数，在不使用负采样的情况下有效地训练了三种最先进的推荐模型，针对序列推荐系统中存在的缺陷，重点解决了序列信息利用率低的问题，并在五个基准数据集上进行了广泛的实验。