  The sequential recommendation aims at predicting the next items in user
behaviors, which can be solved by characterizing item relationships in
sequences. Due to the data sparsity and noise issues in sequences, a new
self-supervised learning (SSL) paradigm is proposed to improve the performance,
which employs contrastive learning between positive and negative views of
sequences.
  However, existing methods all construct views by adopting augmentation from
data perspectives, while we argue that 1) optimal data augmentation methods are
hard to devise, 2) data augmentation methods destroy sequential correlations,
and 3) data augmentation fails to incorporate comprehensive self-supervised
signals.
  Therefore, we investigate the possibility of model augmentation to construct
view pairs. We propose three levels of model augmentation methods: neuron
masking, layer dropping, and encoder complementing.
  This work opens up a novel direction in constructing views for contrastive
SSL. Experiments verify the efficacy of model augmentation for the SSL in the
sequential recommendation. Code is
available\footnote{\url{https://github.com/salesforce/SRMA}}.
