  Sequential Recommendation aims to predict the next item based on user
behaviour. Recently, Self-Supervised Learning (SSL) has been proposed to
improve recommendation performance. However, most of existing SSL methods use a
uniform data augmentation scheme, which loses the sequence correlation of an
original sequence. To this end, in this paper, we propose a Learnable Model
Augmentation self-supervised learning for sequential Recommendation (LMA4Rec).
Specifically, LMA4Rec first takes model augmentation as a supplementary method
for data augmentation to generate views. Then, LMA4Rec uses learnable Bernoulli
dropout to implement model augmentation learnable operations. Next,
self-supervised learning is used between the contrastive views to extract
self-supervised signals from an original sequence. Finally, experiments on
three public datasets show that the LMA4Rec method effectively improves
sequential recommendation performance compared with baseline methods.
