Recent studies suggest that the existing neural models have difficulty handling repeated items in sequential recommendation tasks. However, our understanding of this difficulty is still limited. In this study, we substantially advance this field by identifying a major source of the problem: the single hidden state embedding and static item embeddings in the output softmax layer. Specifically, the similarity structure of the global item embeddings in the softmax layer sometimes forces the single hidden state embedding to be close to new items when copying is a better choice, while sometimes forcing the hidden state to be close to the items from the input inappropriately. To alleviate the problem, we adapt the recently-proposed softmax alternatives such as softmax-CPR to sequential recommendation tasks and demonstrate that the new softmax architectures unleash the capability of the neural encoder on learning when to copy and when to exclude the items from the input sequence. By only making some simple modifications on the output softmax layer for SASRec and GRU4Rec, softmax-CPR achieves consistent improvement in 12 datasets. With almost the same model size, our best method not only improves the average NDCG@10 of GRU4Rec in 5 datasets with duplicated items by 10% (4%-17% individually) but also improves 7 datasets without duplicated items by 24% (8%-39%)!

翻译：近期研究表明，现有神经模型在序列推荐任务中难以处理重复项，然而我们对此难点的理解仍十分有限。本研究通过识别问题的主要来源——输出Softmax层中的单一隐状态嵌入与静态物品嵌入——显著推进了该领域的发展。具体而言，Softmax层全局物品嵌入的相似性结构有时迫使单一隐状态嵌入在复制是更优选择时接近新物品，而另一些情况下则不适当地迫使隐状态接近输入序列中的物品。为缓解此问题，我们将最近提出的Softmax替代方案（如Softmax-CPR）适配至序列推荐任务，并证明新型Softmax架构能够释放神经编码器在学习"何时复制"与"何时排除输入序列物品"方面的能力。仅通过对SASRec和GRU4Rec的输出Softmax层进行简单修改，Softmax-CPR便在12个数据集上取得了一致性提升。在模型尺寸几乎不变的情况下，我们的最佳方法不仅将GRU4Rec在5个含重复物品数据集上的平均NDCG@10提升了10%（个体提升范围为4%-17%），更在7个无重复物品数据集上实现了24%的提升（个体提升范围为8%-39%）！