To Copy, or not to Copy; That is a Critical Issue of the Output Softmax
  Layer in Neural Sequential Recommenders
Haw-Shiuan Chang Nikhil Agarwal Andrew McCallum
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%)!